Amazon Web Services SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Exam Practice Test

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"RESTful web services" => API Gateway.

"EC2 instance receives the raw data, transforms the raw data, and stores all the data in an Amazon S3 bucket" => GLUE with (Extract - Transform - Load)

This option is the most efficient because it uses a gateway VPC endpoint for Amazon S3, which provides reliable connectivity to Amazon S3 without requiring an internet gateway or a NAT device for the VPC1. A gateway VPC endpoint routes traffic from the VPC to Amazon S3 using a prefix list for the service and does not leave the AWS network2. This meets the requirement of not traversing the internet. Option A is less efficient because it uses a private hosted zone by using Amazon Route 53, which is a DNS service that allows you to create custom domain names for your resources within your VPC3. However, this does not provide connectivity to Amazon S3 without an internet gateway or a NAT device. Option C is less efficient because it uses a NAT gateway to access the S3 bucket, which is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances4. However, this does not meet the requirement of not traversing the internet. Option D is less efficient because it uses an AWS Site-to-Site VPN connection between the VPC and the S3 bucket, which is a secure and encrypted network connection between your on-premises network and your VPC. However, this does not meet the requirement of not traversing the internet.

S3 Intelligent-Tiering is a cost-optimized storage class that automatically moves data to the most cost-effective access tier based on changing access patterns. Although it offers cost savings, it also introduces additional latency and retrieval time into the data retrieval process, which may not meet the requirement of "immediately available" data. On the other hand, S3 Standard-Infrequent Access (S3 Standard-IA) provides low cost storage with low latency and high throughput performance. It is designed for infrequently accessed data that can be recreated if lost, and can be retrieved in a timely manner if required. It is a cost-effective solution that meets the requirement of immediately available data and remains accessible for up to 3 months.

AWS Batch is a fully managed service that enables users to run batch jobs on AWS. It can handle different types of tasks written in different languages and run them on EC2 instances. It also integrates with Amazon EventBridge (Amazon CloudWatch Events) to schedule jobs based on time or event triggers. This solution will meet the requirements of performance, scalability and low operational overhead12.

B. Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs. This solution will not meet the requirement of low operational overhead, as it involves converting the EC2 instance to a container and using AWS App Runner, which is a service that automatically builds and deploys web applications and load balances traffic2. This is not necessary for running batch jobs.

C. Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events). This solution will not meet the requirement of performance, as AWS Lambda has a limit of 15 minutes for execution timeand 10 GB for memory allocation3. These limits may not be sufficient for running 1-hour tasks.

D. Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance. This solution will not meet the requirement of low operational overhead, as it involves creating and maintaining AMIs and Auto Scaling groups, which are additional resources that need to be configured and managed2.

Reference URL:

To resolve the issue of slow page loads for a rapidly growing e-commerce website hosted on AWS, a solutions architect can take the following two actions:

1. Set up an Amazon CloudFront distribution

2. Create a read replica for the RDS DB instance

Configuring an Amazon Redshift cluster is not relevant to this issue since Redshift is a data warehousing service and is typically used for the analytical processing of large amounts of data.

Hosting the dynamic web content in Amazon S3 may not necessarily improve performance since S3 is an object storage service, not a web application server. While S3 can be used to host static web content, it may not be suitable for hosting dynamic web content since S3 doesn't support server-side scripting or processing.

Configuring a Multi-AZ deployment for the RDS DB instance will improve high availability but may not necessarily improve performance.

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

This answer is correct because it allows the web tier to access the database tier by using security groups as a source, which is a recommended best practice for VPC connectivity. Security groups are stateful and can reference other security groups in the same VPC, which simplifies the configuration and maintenance of the firewall rules. By adding an inbound rule to the database tier’s security group, the web tier’s EC2 instances can connect to the RDS instance on port 3306, regardless of their IP addresses or subnets.

Lambda server-less is scalable and elastic than EC2 api gateway solution

This option is the most efficient because it uses AWS DataSync, which is a secure, online service that automates and accelerates moving data between on-premises and AWS Storage services1. It also uses DataSync to transfer large amounts of data back and forth between NFS file systems in the two Regions on a periodic basis, which simplifies and speeds up the data transfer process with minimal operational overhead. This solution meets the requirement of transferring large amounts of data back and forth between NFS file systems in the two Regions on a periodic basis with the least operational overhead. Option B is less efficient because it uses AWS Snowball devices, which are physical devices that let you transfer large amounts of data into and out of AWS2. However, this does not provide a periodic data transfer solution, as it requires manual handling and shipping of the devices. Option C is less efficient because it sets up an SFTP server on Amazon EC2, which is a way to provide secure file transfer protocol (SFTP) access to files stored in Amazon S33. However, this does not provide a periodic data transfer solution, as it requires manual initiation and monitoring of the file transfers. Option D is less efficient because it uses AWS Database Migration Service (AWS DMS), which is a service that helps you migrate databases to AWS quickly and securely. However, this does not provide a data transfer solution for NFS file systems, as it only supports relational databases and non-relational data stores.

A. Create an Amazon API Gateway REST API. Configure the method to use the Lambda function. Enable IAM authentication on the API. This option is the most operationally efficient as it allows you to use API Gateway to handle the HTTPS endpoint and also allows you to useIAM to authenticate the calls to the microservice. API Gateway also provides manyadditional features such as caching, throttling, and monitoring, which can be useful for a microservice.

A Network Load Balancer is a type of load balancer that operates at the connection level (Layer 4) and can load balance both TCP and UDP traffic1. A Network Load Balancer is suitable for scenarios where high performance and low latency are required, such as real-time multiplayer games1. A Network Load Balancer can also handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone1.

To meet the requirements of the scenario, the solutions architect should use a Network Load Balancer for traffic distribution between the EC2 instances in the Auto Scaling group. The Network Load Balancer can route UDPtraffic from the client to the servers onthe appropriate port2. TheNetwork Load Balancer can also support TLS offloading for secure communications between the client and servers1.

Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with consistent performance and low latency3. Amazon DynamoDB on-demand is a flexible billing option that requires no capacity planning and charges only for the read and write requests that are performed on the tables3. Amazon DynamoDB on-demand is ideal forscenarios where the application traffic is unpredictable or sporadic, such as gaming applications3.

To meet the requirements of the scenario, the solutions architect should use Amazon DynamoDB on-demand for data storage. Amazon DynamoDB on-demand can store gamer scores and other non-relational data without intervention from the developers. Amazon DynamoDB on-demand can also scale automatically to handle any level of requesttraffic without affecting performance or availability3.

Use AWS Batch on Amazon EC2. AWS Batch is a fully managed batch processing service that can be used to easily run batch jobs on Amazon EC2 instances. It can scale the number of instances to match the workload, allowing the batch job to be completed in the desired time frame with minimal operational overhead.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

AWS Lambda FAQs

By configuring scheduled scaling, the solutions architect can set the Auto Scaling group to automatically scale up to the desired compute level at a specific time (IAM) when the batch job starts and then automatically scale down after the job is complete. This will allow the desired EC2 capacity to be reached quickly and also help in reducing the cost.

This option is the most efficient because it requests an Amazon issued public certificate from AWS Certificate Manager (ACM), which is a service that lets you easily provision,manage, and deploy public and private SSL/TLS certificates for use with AWS services and your internal connected resources1. It also requests the certificate in the us-east-1 Region, which is required for using an ACM certificate with CloudFront2. It also meets therequirement of deploying the certificate without incurring any additional costs, as ACM does not charge for certificates that are used with supported AWS services3. This solution meets the requirement of configuring its CloudFront distribution to use SSL/TLS certificates and using a different domain name for the distribution. Option A is less efficient because it requests an Amazon issued private certificate from ACM, which is a type of certificate that can be used only within your organization or virtual private cloud (VPC). However, this does not meet the requirement of configuring its CloudFront distribution to use SSL/TLS certificates, as CloudFront requires a public certificate. It also requests the certificate in the us-east-1 Region, which is correct. Option B is less efficient because it requests an Amazon issued private certificate from ACM, which is incorrect for the same reason as option A. It also requests the certificate in the us-west-1 Region, which is incorrect as CloudFront requires a certificate in the us-east-1 Region. Option D is less efficient because it requests an Amazon issued public certificate from ACM, which is correct. However, it requests the certificate in the us-west-1 Region, which is incorrect as CloudFront requires a certificate in the us-east-1 Region.

1. Relational database: RDS

2. Container-based applications: ECS

"Amazon ECS enables you to launch and stop your container-based applications by using simple API calls. You can also retrieve the state of your cluster from a centralized service and have access to many familiar Amazon EC2 features."

3. Little manual intervention: Fargate

You can run your tasks and services on a serverless infrastructure that is managed by AWS Fargate. Alternatively, for more control over your infrastructure, you can run your tasks and services on a cluster of Amazon EC2 instances that you manage.

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with a serverless web application. Amazon S3 is a fully managed object storage service that can store any amount of data and serve it over the internet. It supports the following features:

Resilience: Amazon S3 stores data across multiple Availability Zones within a Region, and offers 99.999999999% (11 9’s) of durability. It also supports cross-region replication, which enables automatic and asynchronous copying of objects across buckets in different AWS Regions.

Durability: Amazon S3 encrypts data at rest using server-side encryption with either Amazon S3-managed keys (SSE-S3), AWS KMS keys (SSE-KMS), or customer-provided keys (SSE-C). It also supports encryption in transit using SSL/TLS. Amazon S3 also provides data protection features such as versioning, which keeps multiple versions of an object in the same bucket, and MFA Delete, which requires additional authentication for deleting an object version or changing the versioning state of a bucket.

Performance: Amazon S3 delivers high performance and scalability for serving static and dynamic web content. It also supports features such as S3 Transfer Acceleration, which speeds up data transfers by routing requests to AWS edge locations, and S3 Select, which enables retrieving only a subset of data from an object by using simple SQL expressions.

The S3 Standard-Infrequent Access (S3 Standard-IA) storage class is suitable for storing images that are rarely accessed, but must be immediately available when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost per GB and a higher per-request cost.

Amazon Aurora Serverless for MySQL is a fully managed, auto-scaling relational database service that scales up or down automatically based on the application demand. This service provides all the capabilities of Amazon Aurora, such as high availability, durability, and security, without requiring the customer to provision any database instances. With Amazon Aurora Serverless for MySQL, the sales team can enjoy minimal downtime since the database is designed to automatically scale to accommodate the increased traffic. Additionally, the service allows the customer to pay only for the capacity used, making it cost-effective for infrequent access patterns. Amazon RDS for MySQL could also be an option, but it requires the customer to select an instance type, and the database administrator would need to monitor and adjust the instance size manually to accommodate the increasing traffic.

Amazon EC2 Auto Scaling offers the ability to add lifecycle hooks to your Auto Scaling groups. These hooks let you create solutions that are aware of events in the Auto Scaling instance lifecycle, and then perform a custom action on instances when the corresponding lifecycle event occurs.

S3 One Zone-IA is a storage class that is designed for data that is accessed less frequently, but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. This storage class meets the requirements of the company because it provides a low-cost solution for the secondary copy of its on-premises dataset that would rarely need to be accessed. The other storage classes are either more expensive or not suitable for infrequently accessed data.

 Security groups are virtual firewalls that protect AWS instances and can be applied to EC2, ELB and RDS1. Security groups have rules for inbound and outbound traffic and are stateful, meaning that responses to allowed inbound traffic are allowed to flow out of the instance2. Network ACLs are different from security groups in several ways. They cover entire subnets, not individual instances, and are stateless, meaning that they require rules for both inbound and outbound traffic2. Network ACLs also support deny rules, while security groups only support allow rules2.

To meet the requirements of the scenario, the solutions architect should create two security groups: one for the DB instance and one for the web servers in the public subnet. The security group for the DB instance should allow traffic from the public subnet CIDR block on port 3306, which is the default port for MySQL3. This way, only the web servers in the public subnet can access the DB instance on that port. The security group for the web servers should allow traffic from 0 0 0 O’O on port 443, which is the default port forHTTPS4. This way, the web servers can accept secure connections from the internet on that port.

This answer is correct because it meets the requirements of transitioning to an event-driven architecture, using serverless concepts, and minimizing operational overhead. AWS Step Functions is a serverless service that lets you coordinate multiple AWS services into workflows using state machines. State machines are composed of tasks and transitions that define the logic and order of execution of the workflow steps. AWS Lambda is a serverless function-as-a-service platform that lets you run code without provisioning or managing servers. Lambda functions can be invoked by Step Functions as tasks in a state machine, and can perform different aspects of the data management workflow, such as data ingestion, transformation, validation, and analysis. By using Step Functions and Lambda, the company can benefit from the following advantages:

Event-driven: Step Functions can trigger Lambda functions based on events, such as timers, API calls, or other AWS service events. Lambda functions can also emit events to other services or state machines, creating an event-driven architecture.

Serverless: Step Functions and Lambda are fully managed by AWS, so the company does not need to provision or manage any servers or infrastructure. The company only pays for the resources consumed by the workflows and functions, and can scale up or down automatically based on demand.

Operational overhead: Step Functions and Lambda simplify the development and deployment of workflows and functions, as they provide built-in features such as monitoring, logging, tracing, error handling, retry logic, and security. The company can focus on the business logic and data processing rather than the operational details.

As they would like to retrieve the data with sub-millisecond, DynamoDB with DAX is the answer. DynamoDB supports some of the world's largest scale applications by providing consistent, single-digit millisecond response times at any scale. You can build applications with virtually unlimited throughput and storage.

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does notmeet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

Using Amazon SQS will help minimize the number of connections to the database, as the API will write data to a queue instead of directly to the database. Additionally, using an AWS Lambda function that Amazon SQS invokes to write data from the queue to the database will help ensure that data is not lost during periods of heavy traffic, as the queue will serve as a buffer between the API and the database.

Enforce column-level authorization with Amazon QuickSight and AWS Lake

Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system on all web servers. To meet the requirements of providing a shared file store for Linux-based web servers without making changes to the application, using an Amazon EFS file system is the best solution. Amazon EFS is a managed NFS file system service that provides shared access to files across multiple Linux-based instances, which makes it suitable for this use case. Amazon S3 is not ideal for this scenario since it is an object storage service and not a file system, and it requires additional tools or libraries to mount the S3 bucket as a filesystem. Amazon CloudFront can be used to improve content delivery performance but is not necessary for this requirement. Additionally, Amazon EBS volumes can only be mounted to one instance at a time, so it is not suitable for sharing files across multiple instances.

Key phrase in the Question is must scale read and write capacity. Aurora is only for Read. Amazon DynamoDB has two read/write capacity modes for processing reads and writes on your tables: On-demand Provisioned (default, free-tier

A) Enable AWS CloudTrail and use it for auditing. AWS CloudTrail provides a record of API calls and can be used to audit changes made to EC2 instances and security groups. By analyzing CloudTrail logs, the solutions architect can track who provisioned oversized instances ormodified security groups without proper approval. D) Enable AWS Config and create rules for auditing and compliance purposes. AWS Config can record the configuration changes made to resources like EC2 instances and security groups. The solutions architect can create AWS Config rules to monitor for non-compliant changes, like launching certain instance types or opening security group ports without permission. AWS Config would alert on any violations of these rules.

Amazon S3 is a highly scalable, durable, and cost-effective object storage service that can store millions of images1. Amazon DynamoDB is a fully managed NoSQL database that can handle high throughput and low latency for key-value and document data2. By using S3 to store the images and DynamoDB to store the geographic codes and image S3 URLs, the solution can achieve high availability and scalability during natural disasters. It can alsoleverage DynamoDB’s features such as caching, auto-scaling, and global tables to improve performance and reduce costs2.

A. Store the images and geographic codes in a database table Use Oracle running on an Amazon RDS Multi-AZ DB instance. This solution willnot meet the requirement of scalability and cost-effectiveness, as Oracle is a relational database that may not handle large volumes of unstructured data such as images efficiently3. It also involves higher licensing and operational costs than S3 and DynamoDB12.

C. Store the images and geographic codes in an Amazon DynamoDB table Configure DynamoDB Accelerator (DAX) during times of high load. This solution willnot meet therequirement of cost-effectiveness, as storing images in DynamoDB will consume more storage space and incur higher charges than storing them in S312. It will also require additional configuration and management of DAX clusters to handle high load.

D. Store the images in Amazon S3 buckets Store geographic codes and image S3 URLs in a database table Use Oracle running on an Amazon RDS Multi-AZ DB instance. This solution will not meet the requirement of scalability and cost-effectiveness, asOracle is a relational database that may not handle high throughput and low latency for key-value data such as geographic codes efficiently3. It also involves higher licensing and operational costs than DynamoDB2.

Reference URL:

This solution meets the requirement of reducing the number of database reads while ensuring high availability for a batch application that consists of a backend with multiple Amazon RDS databases. Amazon RDS read replicas are copies of the primary database instance that can serve read-only traffic. You can create one or more read replicas for a primary database instance and connect to them using a special endpoint. Read replicas can improve the performance and availability of your application by offloading read queries from the primary database instance.

Option B is incorrect because using Amazon ElastiCache for Redis can provide a fast, in-memory data store that can cache frequently accessed data, but it does not support replication from Amazon RDS databases. Option C is incorrect because using Amazon Route 53 DNS caching can improve the performance and availability of DNS queries, but it does not reduce the number of database reads. Option D is incorrect because using Amazon ElastiCache forMemcached can provide a fast, in-memory data store that can cache frequently accessed data, but it does not support replication from Amazon RDS databases.

This option is the mostefficient because it sets an overall password policy for the entire AWS account, which is a way to specify complexity requirements and mandatory rotationperiods for IAM user passwords1. It also meets the requirement of setting a password policy for all new users, as the password policy applies to all IAM users in the account. This solution meets the requirement of setting specific complexity requirements and mandatory rotation periods for IAM user passwords. Option B is less efficient because it sets a password policy for each IAM user in the AWS account, which is not possible as password policies can only be set at the account level. Option C is less efficient because it uses third-party vendor software to set password requirements, which is not necessary as IAM provides a built-in way to set password policies. Option D is less efficient because it attaches an Amazon CloudWatch rule to the Create_newuser event to set the password with the appropriate requirements, which is not possible as CloudWatch rules cannot modify IAM user passwords.

Creating a peering connection between the two VPCs and configuring an inbound rule for the ElastiCache cluster's security group to allow inbound connection from the application's security group is the most cost-effective solution. Peering connections are free and you only incur the cost of configuring the security group rules. The Transit VPC solution requires additional VPCs and associated resources, which would incur additional costs.

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"Sensitive data redaction replaces personally identifiable information (PII) in the text transcript and the audio file. A redacted transcript replaces the original text with [PII]; a redacted audio file replaces spoken personal information with silence. This parameter is useful for protecting customer information."

Amazon EFS provides a simple, scalable, fully managed file system that can be simultaneously accessed from multiple EC2 instances and provides built-in redundancy. It is optimized for multiple EC2 instances to access the same files, and it is designed to be highly available, durable, and secure. It can scale up to petabytes of data and can handle thousands of concurrent connections, and is a cost-effective solution for storing and accessing large amounts of data.

Create a t2 micro Amazon EC2 instance. Deploy a LAMP (Linux Apache MySQL, PHP/Perl/Python) stack to host the webpage. Use client-side scripting to build the contact form. Integrate the form with Amazon WorkMail. This solution will provide the company with the necessary components to host the contact form page and integrate it with Amazon WorkMail at the lowest cost. Option A requires the use of Amazon ECS, which is more expensive than EC2, and Option B requires the use of Amazon API Gateway, which is also more expensive than EC2. Option C requires the use of Amazon Lightsail, which is more expensive than EC2.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

AWS Lambda FAQs

Amazon CloudFront is a content delivery network (CDN) that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. CloudFront supports signed URLs that provide authorized access to your content. This feature allows the company to control who can access their content and for how long, providing a secure and scalable solution for millions of users.

Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization.

High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on-premises performance data. Memory optimized instances provide the necessary memory capacity and performance.

Instance Types:

For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.

For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.

Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

Amazon FSx for Lustre: Lustre is a high-performance file system designed for workloads that require fast storage with sustained high throughput and low latency. It integrates with Amazon S3, making it suitable for HPC environments.

Persistent File Systems:

Persistent Storage: Suitable for long-term storage and recurrent use, providing durability and availability.

High Throughput and Low Latency: Persistent Lustre file systems can handle large amounts of data with sub-millisecond latency, meeting the needs of high-performance computing workloads.

Simultaneous Access: FSx for Lustre allows thousands of compute instances to access and process large datasets concurrently, ensuring that the high volume of data is handled efficiently.

Highly Available: FSx for Lustre is designed to provide high availability and is managed by AWS, reducing the operational burden.

AWS Direct Connect: Provides a dedicated network connection from your on-premises data center to AWS, ensuring low latency and consistent network performance.

Direct Connect Gateway Association:

Direct Connect Gateway: Acts as a global network transit hub to connect VPCs across different AWS regions.

Association with Transit Gateway: Enables communication between on-premises data centers and multiple VPCs connected to the transit gateway.

Transit Virtual Interface (VIF):

Create Transit VIF: To connect Direct Connect with a transit gateway.

Setup Steps:

Establish a Direct Connect connection.

Create a transit VIF to the Direct Connect gateway.

Associate the Direct Connect gateway with the transit gateway attached to the VPCs.

Cost Efficiency: This combination avoids the recurring costs and potential performance variability of VPN connections, providing a robust, low-latency hybrid network solution.

Application Load Balancer (ALB): ALB is suitable for routing HTTP/HTTPS traffic to the application servers. It provides advanced routing features and integrates well with Auto Scaling groups.

Target Group Configuration:

Create a target group for the application servers and register the Auto Scaling group with this target group.

Configure the ALB to forward requests from the web servers to the application servers.

Security Group Setup:

Configure the security group of the application servers to only allow traffic from the web servers' security group.

This ensures that only the web servers can access the application servers, meeting the requirement to limit access.

Benefits:

Security: Using security groups to restrict access ensures a secure environment where only intended traffic is allowed.

Scalability: ALB works seamlessly with Auto Scaling groups, ensuring the application can handle varying loads efficiently.

This solution is the most scalable and efficient way to handle large volumes of survey data while automating sentiment analysis:

API Gateway and SQS: The survey results are sent to API Gateway, which forwards the data to an SQS queue. SQS can handle large volumes of messages and ensures that messages are not lost.

AWS Lambda: Lambda is triggered by polling the SQS queue, where it processes the survey data.

Amazon Comprehend: Comprehend is used for sentiment analysis, providing insights into customer satisfaction.

DynamoDB with TTL: Results are stored in DynamoDB with aTime to Live (TTL)attribute set to expire after 365 days, automatically removing old data and reducing storage costs.

Option B (EC2 API): Running an API on EC2 requires more maintenance and scalability management compared to API Gateway.

Option C (S3 and Rekognition): Amazon Rekognition is for image and video analysis, not sentiment analysis.

Option D (Amazon Lex): Amazon Lex is used for building conversational interfaces, not sentiment analysis.

AWS References:

Amazon Comprehend for Sentiment Analysis

Amazon SQS

DynamoDB TTL

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

Option Aallows importing your own encryption keys into AWS KMS, ensuring control over key material.

Option Duses S3 Glacier with SSE-C, where the customer controls the encryption keys, meeting compliance needs.

Option Buses AWS-managed key material, violating the requirement for key material control.

Option C and Eare not fully compliant with the control requirement.

Amazon EFSwithMax I/O performance modeis designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn't support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

Amazon RDS Custom for Oracle: This service allows you to bring your own Oracle Database licenses and provides the flexibility to customize the database settings, making it suitable for applications that require privileged access and third-party database features.

BYOL (Bring Your Own License):

RDS Custom supports the BYOL model, allowing you to use your existing Oracle licenses and comply with licensing requirements.

This helps in leveraging existing investments and reducing migration costs.

Customization and Third-Party Features:

RDS Custom allows for deeper customization of the database environment compared to standard RDS instances.

This makes it possible to support the third-party features that your application relies on without significant changes.

Migration Process:

Use native Oracle tools like Data Pump or RMAN to migrate the database to RDS Custom.

Customize the database settings post-migration to ensure compatibility with third-party features.

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn't traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it's over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn't meet the low-latency requirements of the use case.

This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task's requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler- Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate- Information on how to run containers in a serverless manner using AWS Fargate.

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

This solution allows for secure and least-privilege access with minimal operational overhead.

IAM Identity Center: AWS IAM Identity Center (formerly AWS SSO) enables you to centrally manage access to multiple AWS accounts and applications. By using IAM Identity Center, you can assign permission sets that define what users or groups can access, ensuring that only necessary permissions are granted.

Permission Sets: Permission sets in IAM Identity Center allow you to define granular access controls for specific services, such as Amazon RDS and S3. You can tailor these permissions to meet the needs of different teams, adhering to the principle of least privilege.

Group Management: By assigning users to groups and associating those groups with specific permission sets, you reduce the complexity and overhead of managing individual IAM roles and policies. This method also simplifies compliance and audit processes.

Why Not Other Options?:

Option A (IAM roles): While IAM roles can provide least-privilege access, managing multiple roles and policies across teams increases operational overhead compared to using IAM Identity Center.

Option C (Individual IAM users): Managing individual IAM users and roles can be cumbersome and does not scale well compared to group-based management in IAM Identity Center.

Option D (AWS Organizations with cross-account roles): Creating separate accounts and cross-account roles adds unnecessary complexity and overhead for this use case, where IAM Identity Center provides a more straightforward solution.

AWS References:

AWS IAM Identity Center- Overview and best practices for using IAM Identity Center.

Managing Access Permissions Using IAM Identity Center- Guide on creating and managing permission sets for secure access.

EC2 Account Attributes: Amazon EC2 allows you to set account attributes to automatically encrypt new EBS volumes. This ensures that all new volumes created in your account are encrypted by default.

Configuration Steps:

Go to the EC2 Dashboard.

Select "Account Attributes" and then "EBS encryption".

Enable default EBS encryption and select the default AWS KMS key or a customer-managed key.

Prevention of Unencrypted Volumes: By setting this account attribute, you ensure that it is not possible to create unencrypted EBS volumes, thereby enforcing compliance with security requirements.

Operational Efficiency: This solution requires minimal configuration changes and provides automatic enforcement of encryption policies, reducing operational overhead.

For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure atransient cluster(which runs for the duration of the job and then terminates) withOn-Demand Instancesfor the primary and core nodes, andSpot Instancesfor the task nodes. Here's why:

Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.

Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for non-critical tasks as the framework can handle retries.

Atransient clusteris more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it's not needed.

Option A: A long-running cluster may result in unnecessary costs when the cluster isn't being used.

Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.

Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

AWSSecrets Managercan integrate directly with Amazon RDS for automatic and seamless password rotation. Secrets Manager handles the complexity of password management, including generating strong passwords and rotating them at a defined interval (e.g., every 30 days). It also automatically updates the connection information for RDS, minimizing operational overhead.

Option A (Lambda with EventBridge): While possible, this requires custom coding and operational management of Lambda, which introduces additional complexity.

Option B (Manual password change): Using the modify-db-instance command requires manual intervention and is not automated, leading to more operational effort.

Option D (Parameter Store): Systems Manager Parameter Store is less specialized for password management than Secrets Manager and does not have built-in automated rotation for RDS credentials.

AWS References:

AWS Secrets Manager Rotation for RDS

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

Compute Savings Plansoffer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, usingOn-Demand Instancesensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS's cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

To achieve high availability for the website, two key actions should be taken:

Amazon RDS for MySQL Multi-AZ: By migrating the database to an RDS for MySQL Multi-AZ deployment, the database becomes highly available. Multi-AZ provides automatic failover from the primary database to a standby replica in another Availability Zone, ensuring database availability even in the case of an AZ failure.

Application Load Balancer and Auto Scaling: Deploying an Application Load Balancer (ALB) in front of the EC2 instances ensures that traffic is evenly distributed across the instances. Configuring an Auto Scaling group to run EC2 instances across multiple Availability Zones ensures that the application remains available even if one instance or one AZ becomes unavailable. This setup enhances fault tolerance and improves reliability.

Why Not Other Options?:

Option A (Internet Gateway per AZ): Internet Gateways are region-wide resources and do not need to be provisioned per Availability Zone. This option does not contribute to high availability.

Option C (DynamoDB + Auto Scaling): DynamoDB would require changes to the application code to switch from MySQL, which is not possible per the question's constraints.

Option D (DataSync): AWS DataSync is used for data transfer and synchronization, not for achieving high availability for a database.

AWS References:

Amazon RDS Multi-AZ Deployments- Explanation of how Multi-AZ deployments work in Amazon RDS.

Application Load Balancing- Details on how to configure and use ALB for distributing traffic across multiple instances.

For an application requiring TCP communication and high availability:

Network Load Balancer (NLB)is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling groupensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

AWS Application Migration Service (AWS MGN) is the recommended tool for a lift-and-shift strategy, especially for time-sensitive migrations. It automates the replication of on-premises VMs to AWS, minimizing the effort required for migration and testing.

Key steps:

Replication with AWS MGN: The AWS Replication Agent is installed on the VMs to continuously replicate data to AWS, allowing you to manage migration easily.

Testing and Cutover: Initial replication allows for testing in AWS before performing the final cutover, ensuring that the migration process is smooth and data integrity is maintained.

AWS Documentation: AWS MGN is recommended for migrating virtual machines to the cloud with minimal downtime and disruption​​.

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn't require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

AWS Lambda for Operational Efficiency:

AWS Lambdais a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDBis a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using theDynamoDB Scan APIor queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS)is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sendingout email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function usingAmazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS)is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, whereSNSis the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

CloudFront Signed URLs: These URLs allow you to provide limited access to content that is being served through an Amazon CloudFront distribution. Signed URLs can be generated to grant time-limited access to premium customers.

Content Restriction:

By using CloudFront signed URLs, you can control access to your media streams and file content stored in S3.

These URLs can be customized with an expiration time, ensuring that access is only available for a specific period, which is useful for scenarios like movie rentals or music downloads.

Security and Flexibility:

Signed URLs ensure that only authenticated users (premium customers) can access the restricted content.

This approach integrates seamlessly with CloudFront and S3, providing an efficient way to manage access controls without additional overhead.

Operational Efficiency: Using CloudFront signed URLs leverages AWS managed services to handle the complexity of access control, reducing the need for custom implementation and maintenance.

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

Option Auses an IAM role attached to the EC2 instance profile, enabling secure and automated access to Secrets Manager. This is the recommended approach.

Option Buses IAM users, which is less secure and harder to manage.

Option Cis not practical for accessing secrets programmatically.

Option Dviolates best practices by granting direct access to the EC2 instance.

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Amazon Cognitoprovides scalable, serverless authentication, andLambda@Edgeis used for authorization, providing low-latency access control at the edge.Amazon CloudFrontserves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.

Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.

Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.

Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.

AWS References:

Amazon Cognito

Lambda@Edge

VPC Endpoint for S3: A gateway endpoint for Amazon S3 enables you to privately connect your VPC to S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Configuration Steps:

In the VPC console, navigate to "Endpoints" and create a new endpoint.

Select the service name for S3 (com.amazonaws.region.s3).

Choose the VPC and the subnets where your EC2 instances are running.

Update the route tables for the selected subnets to include a route pointing to the endpoint.

Security Compliance: By configuring an S3 gateway endpoint, all traffic between the VPC and S3 stays within the AWS network, complying with the company's security regulations to avoid internet traversal.

To upload photos to an S3 bucket using Amazon CloudFront with a custom domain name, the following components are required:

ACM in us-east-1 (Option A): When using CloudFront with HTTPS, the SSL/TLS certificate must be created in theus-east-1Region. AWS Certificate Manager (ACM) handles the provisioning, management, and renewal of public certificates, making this a cost-effective and low-maintenance solution.

S3 Transfer Acceleration (Option C): Transfer Acceleration allows faster uploads to S3 from CloudFront by routing traffic through AWS’s edge locations. This significantly speeds up the data upload process, especially for users that are geographically distant from the S3 bucket's region.

Option B (ACM in eu-west-1): CloudFront only supports certificates created in us-east-1.

Option D and E (OAC and website endpoint): These are not ideal for handling secure uploads or efficient data transfers in this case.

AWS References:

Using ACM with CloudFront

Amazon S3 Transfer Acceleration

Amazon API Gateway supportsresource policies, which allow you to control access to your API by specifying the IP addresses or ranges that can access the API. By creating a resource policythat explicitly denies access to any IP address outside the allowed set, you can ensure that only trusted IP addresses (such as those from your internal network) can access the critical information in your API. This approach provides fine-grained access control without the need for additional infrastructure or complex configurations.

Option A (Private integration): API Gateway private integrations are for creating private APIs that are only accessible within a VPC, but this solution is about restricting access to certain IP addresses.

Option C (Private subnet and ACLs): Deploying the API in a private subnet and using network ACLs adds unnecessary complexity and isn't the best fit for HTTP APIs.

Option D (Security group): API Gateway doesn't have a security group because it isn't a resource inside a VPC. Instead, resource policies are the correct mechanism for controlling IP-based access.

AWS References:

Controlling Access to API Gateway with Resource Policies

SSE-KMS: Server-side encryption with AWS Key Management Service (SSE-KMS) provides robust encryption of data at rest, integrated with AWS KMS for key management and auditing.

Automatic Key Rotation: By enabling automatic rotation for the KMS keys, the system ensures that keys are rotated annually without manual intervention, meeting compliance requirements.

Logging and Auditing: AWS KMS automatically logs all key usage and management actions in AWS CloudTrail, providing the necessary audit logs.

Implementation:

Create a KMS key with automatic rotation enabled.

Configure the S3 bucket to use SSE-KMS with the created KMS key.

Ensure CloudTrail is enabled for logging KMS key usage.

Operational Efficiency: This solution provides encryption, automatic key management, and auditing in a seamless, fully managed way, reducing operational overhead.

Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

An S3 Bucket Key reduces the cost of using AWS KMS for server-side encryption by decreasing the number of requests made to KMS. By enabling S3 Bucket Key, the company can meet its encryption requirements with KMS keys while optimizing costs by reducing the number of KMS API requests.

Key AWS features:

Cost Optimization: S3 Bucket Keys reduce the frequency of KMS calls, optimizing the cost associated with encryption while still using AWS KMS for key management.

Compliance with KMS Encryption: This solution continues to meet the strict encryption requirements of the company by using KMS managed keys.

AWS Documentation: Using an S3 Bucket Key is recommended for organizations looking to optimize encryption costs without compromising security​​.

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don't provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn't offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

Amazon S3 is cheapest and can be accessed from anywhere.

Cloudfront for rapid response and s3 to minimize infrastructure.

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestrationservice that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service(Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

A -> We can configure CloudFront to require HTTPS from clients (enhanced security) D - > storing static website on S3 provides scalability and less operational overhead, then configuration of Application LB and EC2 instances (hence E is out)

This solution meets the requirement of migrating a Windows-based application that requires the use of a shared Windows file system attached to multiple Amazon EC2 Windows instances that are deployed across multiple Availability Zones. Amazon FSx for Windows File Server provides fully managed shared storage built on Windows Server, and delivers a wide range of data access, data management, and administrative capabilities. It supports the Server Message Block (SMB) protocol and can be mounted to EC2 Windows instances across multiple Availability Zones.

Option A is incorrect because AWS Storage Gateway in volume gateway mode provides cloud-backed storage volumes that can be mounted as iSCSI devices from on-premises application servers, but it does not support SMB protocol or EC2 Windows instances. Option C is incorrect because Amazon Elastic File System (Amazon EFS) provides a scalable and elastic NFS file system for Linux-based workloads, but it does not support SMB protocol or EC2 Windows instances. Option D is incorrect because Amazon Elastic Block Store (Amazon EBS) providespersistent block storage volumes for use with EC2 instances, but it does not support SMB protocol or attaching multiple instances to the same volume.

Q: What does Amazon RDS manage on my behalf?

Amazon RDS manages the work involved in setting up a relational database: from provisioning the infrastructure capacity you request to installing the database software. Once your database is up and running, Amazon RDS automates common administrative tasks such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

1. database shows no signs of being overloaded. CPU, memory, and disk access metrics are all low==>A and C out. We cannot only add nodes instance or add read replica, because database workload is totally fine, very low. 2. "least operational overhead"==>B out, because b need to configure lambda. 3. ROS proxy: Shares infrequently used connections; High availability with failover; Drives increased efficiency==>proxy can leverage failover to redirect traffic from timeout rds instance to healthy rds instance. So D is right.

Queue has Limited throughput (300 msg/s without batching, 3000 msg/s with batching whereby up-to 10 msg per batch operation; Msg duplicates not allowed in the queue (exactly-once delivery); Msg order is preserved (FIFO); Queue name must end with .fifo

This solution meets the requirements of measuring the effectiveness of marketing campaigns by performing batch processing on csv files of sales data and storing the results in an Amazon S3 bucket once every hour. An AWS duo ETL process can use services such as AWS Glue or AWS Data Pipeline to extract data from S3, transform it into a more efficient format such as Apache Parquet, and load it back into S3. Apache Parquet is a columnar storage format that can improve the query performance and reliability of Athena by reducing the amount of data scanned, improving compression ratio, and enabling predicate pushdown.

This solution meets the requirements of saving money on storage while keeping the most accessed files readily available for the users. S3 Lifecycle policy can automatically move objects from one storage class to another based on predefined rules. S3 Standard-IA is a lower-cost storage class for data that is accessed less frequently, but requires rapid access when needed. It is suitable for ringtones older than 90 days that are downloaded infrequently.

Option A is incorrect because configuring S3 Standard-IA for the initial storage tier of the objects can incur higher costs for frequent access and retrieval fees. Option B is incorrect because moving the files to S3 Intelligent-Tiering can incur additional monitoring and automation fees that may not be necessary for ringtones older than 90 days. Option C is incorrect because using S3 inventory to manage objects and move them to S3 Standard-IA can be complex and time-consuming, and it does not provide automatic cost savings.

For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions.

To make all the data available to various teams and minimize operational overhead, the company can create a data lake by using AWS Lake Formation. This will allow the company to centralize all the data in one place and use fine-grained access controls to manage access to the data. To meet the requirements of the company, the solutions architect can create a data lake by using AWS Lake Formation, create an AWS Glue JDBC connection to Amazon RDS, and register the S3 bucket in Lake Formation. The solutions architect can then use Lake Formation access controls to limit access to the data. This solution will provide the ability to manage fine-grained permissions for the data and minimize operational overhead.

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

This solution meets the requirements of directing users to a backup static error page if the primary website is unavailable, minimizing changes and infrastructure overhead. Route 53active-passive failover configuration can route traffic to a primary resource when it is healthy or to a secondary resource when the primary resource is unhealthy. Route 53 health checks can monitor the health of the ALB endpoint and trigger the failover when needed. The static error page can be hosted in an S3 bucket that is configured as a website, which is a simple and cost-effective way to serve static content.

Option A is incorrect because using a latency routing policy can route traffic based on the lowest network latency for users, but it does not provide failover functionality. Option C is incorrect because using an active-active configuration with the ALB and an EC2 instance can increase the infrastructure overhead and complexity, and it does not guarantee that the EC2 instance will always be healthy. Option D is incorrect because using a multivalue answer routing policy can return multiple values for a query, but it does not provide failover functionality.

EC2 Spot Instances allow users to bid on spare Amazon EC2 computing capacity and can be a cost-effective solution for stateless, interruptible workloads that can be started and stopped at any time. Since the batch processing job is stateless, can be started and stopped at any time, and typically takes upwards of 60 minutes to complete, EC2 Spot Instances would be a good fit for this workload.

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created.EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

Amazon CloudFront is a content delivery network (CDN) that speeds up the delivery of static and dynamic web content, such as HTML pages, images, and videos. By using CloudFront, the HTML pages will be served to users from the edge location that is closest to them, resulting in faster delivery and a better user experience. CloudFront can also handle the high traffic and large number of requests expected for the global event, ensuring that the HTML pages are available and accessible to users around the world.

Under "Encrypt unencrypted resources"

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverageexisting AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

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FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can't be tolerated. Examples of situations where you might use FIFO queues include the following: To make sure that user-entered commands are run in the right order. To display the correct product price by sending price modifications in the right order. To prevent a student from enrolling in a course before registering for an account.

This solution meets the requirements of a disaster recovery solution to back up the data that is generated by an analytics application, stored in JSON format, and must be accessible in milliseconds if it is needed. Amazon S3 Standard is a durable and scalable storage class for frequently accessed data. It can store any amount of data and provide high availability and performance. It can also support millisecond access time for data retrieval.

Option A is incorrect because Amazon OpenSearch Service (Amazon Elasticsearch Service) is a search and analytics service that can index and query data, but it is not a backup solution for data stored in JSON format. Option B is incorrect because Amazon S3 Glacier is a low-cost storage class for data archiving and long-term backup, but it does not support millisecond access time fordata retrieval. Option D is incorrect because Amazon RDS for PostgreSQL is a relational database service that can store and query structured data, but it is not a backup solution for data stored in JSON format.

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect becausepurchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

By using an SQS queue and Lambda, the solutions architect can decouple the API front end from the processing microservices and improve the overall scalability and availability of the system. The SQS queue acts as a buffer, allowing the API front end to continue accepting user requests even if the processing microservices are experiencing high workloads or are temporarilyunavailable. The Lambda function can then retrieve requests from the SQS queue and write them to DynamoDB, ensuring that all user requests are stored and processed. This approach allows the company to scale the processing microservices independently from the API front end, ensuring that the API remains available to users even during periods of high demand.

The policy is separated into two parts because the ListBucket action requires permissions on the bucket while the other actions require permissions on the objects in the bucket. You must use two different Amazon Resource Names (ARNs) to specify bucket-level and object-level permissions. The first Resource element specifies arn:aws:s3:::AdminTools for the ListBucket action so that applications can list all objects in the AdminTools bucket.

1. lowest possible latency + node to node ==> cluster placement(must be within one AZ), so C, D out

2. For EBS Multi-Attach, up to 16 instances can be attached to a single volume==>we have 16 linux instance==>more close to A

3. "need a shared block device volume"==>EBS Multi-attach is Block Storage whereas EFS is File Storage==> B out

4. EFS automatically replicates data within and across 3 AZ==>we use cluster placement so all EC2 are within one AZ.

5. EBS Multi-attach volumes can be used for clients within a single AZ.

This approach ensures that the order creation and payment processing steps are separate and atomic. By sending the order information to an SQS FIFO queue, the payment service can process the order one at a time and in the order they were received. If the payment service is unable to process an order, it can be retried later, preventing the creation of multiple orders. The deletion of the message from the queue after it is processed will prevent the same message from being processed multiple times.

Requirement Analysis: The company needs to scale throughput for uploading large files to S3 and downloading them to EC2 instances.

S3 Multipart Uploads: This method allows for the parallel upload of parts of a file, improving upload efficiency and reliability.

Parallel Fetching: Fetching multiple byte-ranges in parallel from S3 improves download performance.

Implementation:

For uploads, use the S3 multipart upload API to upload files in parallel.

For downloads, use the S3 API to request multiple byte-ranges concurrently.

Conclusion: These solutions effectively scale throughput and improve the performance of both uploads and downloads.

References

S3 Multipart Upload:Amazon S3 Multipart Upload

Parallel Fetching:S3 Byte-Range Fetches

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Understanding the Requirement: The robotics company needs a public load balancer to ensure seamless communication with backend services, route traffic based on query strings, and encrypt traffic.

Analysis of Options:

Network Load Balancer with ACM Certificate: NLBs primarily operate at the transport layer (Layer 4) and do not natively support query parameter-based routing, which is a Layer 7 feature.

Gateway Load Balancer with IAM Certificate: Gateway Load Balancers are designed for deploying, scaling, and managing third-party virtual appliances and do not support HTTP path-based or query parameter-based routing.

Application Load Balancer with ACM Certificate: ALBs operate at the application layer (Layer 7), supporting features like query parameter-based routing and SSL/TLS termination with ACM certificates.

Network Load Balancer with IAM Certificate: As with the first option, NLBs do not support query parameter-based routing, making it unsuitable for this requirement.

Best Solution:

Application Load Balancer with ACM Certificate: This option provides the necessary Layer 7 routing capabilities and SSL/TLS termination to meet the requirements for query parameter-based routing and encrypted communication.

Understanding the Requirement: The data must be encrypted at rest with automatic key rotation every year, with minimal operational overhead.

Analysis of Options:

SSE-S3: This option provides encryption with S3 managed keys and automatic key rotation but offers less control and flexibility compared to KMS keys.

AWS KMS with Customer Managed Key (automatic rotation): This option offers full control over encryption keys, with AWS KMS handling automatic key rotation, minimizing operational overhead.

AWS KMS with Customer Managed Key (manual rotation): This requires manual intervention for key rotation, increasing operational overhead.

Customer Key Material: This involves more complex management, including importing key material and setting up automatic rotation, which increases operational overhead.

Best Option for Minimal Operational Overhead:

AWS KMS with a customer managed key and automatic rotation provides the needed security and key rotation with minimal operational effort. Setting the S3 bucket’s default encryption to use this key ensures all data is encrypted as required.

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

AWS Outposts is a fully managed service that delivers AWS infrastructure and services to virtually any on-premises or edge location for a consistent hybrid experience. AWS Outposts supports Amazon EKS, which is a managed service that makes it easy to run Kubernetes on AWS and on-premises. By installing an AWS Outposts rack in the company’s data center, the company can run containers in a Kubernetes environment using Amazon EKS and other AWS managed services, while keeping the data locally in the company’s data center and meeting the compliance requirements. AWS Outposts also provides a seamless connection to the local AWS Region for access to a broad range of AWS services.

Option A is not a valid solution because AWS Local Zones are not deployed in the company’s data center, but in large metropolitan areas closer to end users. AWS Local Zones are owned, managed, and operated by AWS, and they provide low-latency access to the public internet and the local AWS Region. Option B is not a valid solution because AWS Snowmobile is a service that transports exabytes of data to AWS using a 45-foot long ruggedized shipping container pulled by a semi-trailer truck. AWS Snowmobile is not designed for running containers or AWS managed services on-premises, but for large-scale data migration. Option D is not a valid solution because AWS Snowball Edge Storage Optimized is a device that provides 80 TB of HDD or 210 TB of NVMe storage capacity for data transfer and edge computing. AWS Snowball Edge Storage Optimized does not support Amazon EKS or other AWS managed services, and it is not suitable for running containers in a Kubernetes environment.

Understanding the Requirement: The company wants to optimize costs and reduce operational overhead for an RDS for PostgreSQL database that only needs to be active during business hours on weekdays.

Analysis of Options:

Instance Scheduler on AWS: Allows for automated start and stop schedules based on specified times, ideal for resources only needed during certain hours. This directly optimizes costs by running the database only when needed.

Turn off automatic backups and create weekly snapshots: Does not address the requirement of reducing operational overhead and optimizing runtime costs.

Custom Lambda function: This could work but adds unnecessary complexity compared to using the Instance Scheduler.

All Upfront Reserved DB Instances: While this reduces costs, it does not optimize for usage patterns that require the database only during specific hours.

Best Solution:

Instance Scheduler on AWS: This option effectively manages the database runtime based on the specified schedule, reducing costs and operational overhead.

Understanding the Requirement: The company needs a container solution that can scale across on-premises, hybrid, and cloud environments, with a load balancer for HTTP traffic.

Analysis of Options:

Fargate Launch Type and ECS Anywhere: Using Fargate for cloud-based containers and ECS Anywhere for on-premises containers provides a unified management experience across environments without needing to manage infrastructure.

Application Load Balancer: Suitable for HTTP traffic and can distribute requests to the ECS services, ensuring scalability and performance.

Network Load Balancer: Typically used for TCP/UDP traffic, not specifically optimized for HTTP traffic.

EC2 Launch Type for ECS and ECS Anywhere with Fargate: Involves managing infrastructure for EC2 instances, increasing operational overhead.

Best Combination of Solutions:

ECS with Fargate Launch Type and ECS Anywhere: This provides flexibility and scalability across hybrid environments with minimal operational overhead.

Application Load Balancer: Optimized for HTTP traffic, ensuring efficient load distribution and scaling for the ECS services.

Understanding the Requirements: The company needs to optimize costs and has the flexibility to change EC2 instance types and families frequently (every 2-3 months).

Savings Plans Overview: Savings Plans offer significant savings over On-Demand pricing, with the flexibility to use any instance type and family within a region.

No Upfront Compute Savings Plan: This plan allows for cost optimization without any upfront payment, offering flexibility to change instance types and families.

Term Selection: A 1-year term is appropriate for balancing cost savings and flexibility given the frequent changes in instance types.

Conclusion: A No Upfront Compute Savings Plan for a 1-year term provides the needed flexibility and cost savings without the commitment and inflexibility of Reserved Instances.

References

AWS Savings Plans:AWS Savings Plans

AWS Cost Management Documentation:AWS Cost Management

Understanding the Requirement: The company has petabytes of data in S3 Standard across multiple buckets with varying access frequencies. They do not know the access patterns and need a cost-optimized storage solution with minimal operational effort.

Analysis of Options:

S3 Intelligent-Tiering: This storage class automatically moves data between two access tiers (frequent and infrequent) based on changing access patterns. It incurs a small monitoring and automation charge but eliminates the need to manually move data between storage classes.

S3 Storage Class Analysis Tool: While useful for determining access patterns, this tool requires manual intervention to move objects to the appropriate storage class, which increases operational overhead.

S3 Glacier Instant Retrieval: This storage class is designed for data that is rarely accessed but requires instant retrieval when needed. It may not be suitable for data with unknown and varying access patterns.

S3 One Zone-IA: This is a lower-cost option for infrequently accessed data stored in a single availability zone. It does not provide the same level of durability and availability as other options and requires knowledge of access patterns.

Best Option for Operational Efficiency:

S3 Intelligent-Tiering provides the best balance of cost savings and operational efficiency. It dynamically adjusts to access patterns without manual intervention, ensuring the company is only paying for what they need without the risk of incurring high costs for infrequent access data.

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

Understanding the Requirement: The company needs to redesign the architecture to be highly available and use AWS managed solutions for hosting a web application with static content, PHP application, and Redis for user sessions.

Analysis of Options:

AWS Elastic Beanstalk: Suitable for simplifying deployment but may not provide the desired flexibility and control for complex architectures.

AWS Lambda and API Gateway: Not ideal for hosting a stateful PHP application and handling static content.Adding complexity without significant benefit.

EC2 instance with ElastiCache and S3: Provides some high availability but involves managing EC2 instances, which increases operational overhead.

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This solution leverages fully managed AWS services for each component, ensuring high availability and scalability.

Best Solution:

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This combination of services meets the requirements for a highly available and managed solution, ensuring optimal performance and minimal operational overhead.

Requirement Analysis: The company needs highly available, scalable storage for a document management application without modifying the application during migration.

EFS Overview: Amazon EFS provides scalable file storage that can be mounted concurrently on multiple EC2 instances across different Availability Zones.

EFS Standard-IA: Using the Standard-IA storage class helps reduce costs for infrequently accessed data while maintaining high availability and scalability.

Implementation:

Create an EFS file system.

Configure mount targets in multiple Availability Zones to ensure high availability.

Mount the EFS file system on EC2 instances in the Auto Scaling group.

Conclusion: This solution meets the high availability, scalability, and cost-effectiveness requirements without needing application modifications.

References

Amazon EFS:Amazon EFS Documentation

EFS Storage Classes:Amazon EFS Storage Classes

 This solution meets the requirements with the least operational overhead because it uses AWS services that are fully managed and scalable. The EventBridge rule can detect the DeleteKey operation from the AWS KMS API and trigger the Systems Manager Automation runbook, which can execute a predefined workflow to cancel the key deletion. The EventBridge rule can also publish an SNS message to the topic that sends an email notification to the administrators. This way, the company can prevent the accidental deletion of KMS keys and notify the administrators of any attempts to delete them.

Option A is not a valid solution because AWS Config rules are used to evaluate the configuration of AWS resources, not to cancel the deletion of KMS keys. Option B is not a valid solution because it requires creating and maintaining a custom Lambda function that has logic to prevent KMS key deletion, which adds operational overhead. Option D is not a valid solution because it only notifies the administrators of the DeleteKey operation, but does not cancel it.

Understanding the Requirement: The company wants to centrally collect security data with minimal development effort to assess and improve security across all workloads.

Analysis of Options:

Amazon Security Lake: This is a purpose-built service for centralizing security data from across AWS services and third-party sources into a data lake. It provides native integration and requires minimal development effort to set up.

AWS Lake Formation with AWS Glue: While this can be used to create a data lake, it requires more development effort to set up and configure Glue crawlers for ingestion.

AWS Lambda with S3: This approach involves custom development to collect and process security data before storing it in S3, which requires more effort.

AWS DMS to RDS: AWS Database Migration Service is typically used for database migrations and is not suited for collecting and analyzing security data.

Best Option for Minimal Development Effort:

Amazon Security Lake provides the least development effort for setting up a centralized repository for security data. It simplifies data ingestion and management, making it the most efficient solution for this use case.

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

Understanding the Requirement: The company needs all EC2 instances to share up-to-date website content with minimal lag time, running behind an Application Load Balancer.

Analysis of Options:

EC2 User Data with ALB: Complex and not scalable as it requires updating each instance manually.

Amazon EFS: Provides a scalable, shared file storage solution that can be mounted by multiple EC2 instances, ensuring all instances have access to the same up-to-date content.

Amazon S3 with EC2 Sync: Involves periodic synchronization which introduces lag and complexity.

Amazon EBS Snapshots: Not suitable for dynamic and frequent updates required by a content management system.

Best Solution:

Amazon EFS: Ensures all EC2 instances have access to a consistent and up-to-date set of website assets with minimal lag time, meeting the requirements effectively.

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

Requirement Analysis: The company wants to identify cost optimizations for EC2 instances, the Auto Scaling group, and EBS volumes with high operational efficiency.

AWS Compute Optimizer: This service provides actionable recommendations to help optimize your AWS resources, including EC2 instances, Auto Scaling groups, and EBS volumes.

Cost Recommendations: Compute Optimizer analyzes the utilization of resources and provides specific recommendations for rightsizing or optimizing the configurations.

Operational Efficiency: Using Compute Optimizer automates the process of identifying cost-saving opportunities, reducing the need for manual analysis.

Implementation:

Enable AWS Compute Optimizer for your AWS account.

Review the recommendations provided for EC2 instances, Auto Scaling groups, and EBS volumes.

Conclusion: This solution provides a comprehensive, automated approach to identifying cost optimizations with minimal operational effort.

References

AWS Compute Optimizer:AWS Compute Optimizer Documentation

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet.Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

Understanding the Requirement: The company needs to transfer 50 TB of data to AWS with minimal operational overhead and no available network bandwidth for the transfer. The transformation job must continue running in the AWS Cloud.

Analysis of Options:

AWS DataSync and AWS Glue: DataSync is suitable for online data transfer, but there is no available network bandwidth. AWS Glue can be used for data transformation but does not solve the bandwidth issue.

AWS Snowcone: Snowcone is a smaller device suitable for smaller data transfers, and deploying the transformation application on it may not be feasible for 50 TB of data.

AWS Snowball Edge Storage Optimized with Glue: This device is designed for large data transfers. Copying the data to the device is straightforward, and AWS Glue can handle data transformation in the cloud.

AWS Snowball Edge Storage Optimized with EC2: This involves setting up EC2 instances for transformation, adding operational complexity compared to using AWS Glue.

Best Solution:

AWS Snowball Edge Storage Optimized with AWS Glue: This provides the least operational overhead for transferring large amounts of data and setting up the transformation job in the cloud.

Requirement Analysis: The Lambda function in the administrator account needs to process messages from an SNS topic in the production account.

IAM Policy for SNS Topic: Allows the Lambda function to subscribe and be invoked by the SNS topic.

SQS Queue for Buffering: Using an SQS queue provides reliable message delivery and buffering between SNS and Lambda, ensuring all messages are processed.

Implementation:

Create an SQS queue in the administrator account.

Set an IAM policy to allow the Lambda function to subscribe to and be invoked by the SNS topic.

Configure the SNS topic to send messages to the SQS queue.

Set up the SQS queue to trigger the Lambda function.

Conclusion: This solution ensures reliable message delivery and processing with appropriate permissions.

References

Amazon SNS:Amazon SNS Documentation

Amazon SQS:Amazon SQS Documentation

AWS Lambda:AWS Lambda Documentation

Understanding the Requirement: The company needs to store a copy of all new photos in the us-east-1 Region from an S3 bucket in the us-west-1 Region.

Analysis of Options:

Cross-Region Replication: Automatically replicates objects across regions with minimal operational effort once configured.

CORS Configuration: Used for allowing resources on a web page to be requested from another domain, not for replication.

S3 Lifecycle Rule: Manages the transition of objects between storage classes within the same bucket, not for cross-region replication.

S3 Event Notifications with Lambda: Requires additional configuration and management compared to Cross-Region Replication.

Best Solution:

S3 Cross-Region Replication: This solution provides an automated and efficient way to replicate objects to another region, meeting the requirement with the least operational effort.

Current Backup Settings: By default, Amazon RDS creates automated backups with a retention period of 7 days.

Regulatory Requirements: The requirement is to retain daily backups for 30 days.

Adjusting Retention Period: You can modify the RDS instance settings to increase the automated backup retention period to 30 days.

Operational Overhead: This solution is the simplest as it leverages existing automated backups and requires minimal intervention.

Implementation: The change can be made via the AWS Management Console, AWS CLI, or AWS SDKs.

References

Amazon RDS Backups:Amazon RDS Documentation

Understanding the Requirement: The application running on EC2 instances in a private subnet needs to process sensitive information from an S3 bucket without using the internet.

Analysis of Options:

Internet Gateway: This would expose the application to the internet, which is not suitable for accessing sensitive information securely.

VPN Connection: VPN is primarily used for secure connections between on-premises networks and AWS VPCs, not for direct S3 access within the same VPC.

NAT Gateway: This allows instances in a private subnet to connect to the internet, but the goal is to avoid internet access.

VPC Endpoint: Provides a private connection between the VPC and S3 without using the internet, ensuring secure access to the S3 bucket.

Best Solution:

VPC Endpoint: Configuring a VPC endpoint allows secure, private communication between the EC2 instances and the S3 bucket without using the internet, ensuring data security and compliance.

This solution meets the requirements most cost-effectively because it enables the company to migrate its on-premises NFS data store to AWS without changing the existing applications or workflows. AWS Storage Gateway is a hybrid cloud storage service that provides seamless and secure integration between on-premises and AWS storage. Amazon S3 File Gateway is a type of AWS Storage Gateway that provides a file interface to Amazon S3, with local caching for low-latency access. By setting up an Amazon S3 File Gateway, the company can store and retrieve files as objects in Amazon S3 using standard file protocols such as NFS. The company can also use an Amazon S3 Lifecycle policy to automatically transition the data to the appropriate storage class based on the frequency of access and the cost of storage. For example, the company can use S3 Standard for frequently accessed data, S3 Standard-Infrequent Access (S3 Standard-IA) or S3 One Zone-Infrequent Access (S3 One Zone-IA) for less frequently accessed data, and S3 Glacier or S3 Glacier Deep Archive for long-term archival data.

Option A is not a valid solution because AWS Storage Gateway Volume Gateway is a type of AWS Storage Gateway that provides a block interface to Amazon S3, with local caching for low-latency access. Volume Gateway is not suitable for migrating an NFS data store, as it requires attaching the volumes to EC2 instances or on-premises servers using the iSCSI protocol. Option C is not a valid solution because Amazon Elastic File System (Amazon EFS) is a fully managed elastic NFS file system that is designed for workloads that require high availability, scalability, and performance. Amazon EFS Standard-Infrequent Access (Standard-IA) is a storage class within Amazon EFS that is optimized for infrequently accessed files, with a lower price per GB and a higher price per access. Using Amazon EFS Standard-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. Option D is not a valid solution because Amazon EFS One Zone-Infrequent Access (One Zone-IA) is a storage class within Amazon EFS that isoptimized for infrequently accessed files that do not require the availability and durability of Amazon EFS Standard or Standard-IA. Amazon EFS One Zone-IA stores data in a single Availability Zone, which reduces the cost by 47% compared to Amazon EFS Standard-IA, but also increases the risk of data loss in the event of an Availability Zone failure. Using Amazon EFS One Zone-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. It would also compromise the availability and durability of the data.

Understanding the Requirement: The company needs to keep daily EBS snapshots for 1 month and retain monthly snapshots for 7 years due to new regulations.

Analysis of Options:

S3 Glacier Deep Archive: Moving snapshots to S3 Glacier Deep Archive involves additional complexity and might not be the most straightforward approach for EBS snapshots.

EBS Snapshots Archive: This is a cost-effective solution designed specifically for long-term storage of EBS snapshots.

Standard Tier for 7 Years: Keeping snapshots in the standard tier for 7 years is more expensive and does not optimize costs.

EBS Direct APIs to S3: This involves additional operational overhead and is not the most cost-effective approach compared to using EBS Snapshots Archive.

Best Solution:

EBS Snapshots Archive: Adding a policy to move monthly snapshots to the EBS Snapshots Archive for long-term retention is the most cost-effective and administratively simple solution.

Understanding the Requirement: The application running on EC2 instances in private subnets needs frequent access to large confidential files stored in S3, minimizing data transfer costs.

Analysis of Options:

Gateway Endpoint for S3: Provides a secure, scalable, and cost-effective way for instances in private subnets to access S3 without using the internet or NAT gateways, thus minimizing data transfer costs.

Single NAT Gateway: Incurs additional costs for data transfer through the NAT gateway, which is not cost-effective.

PrivateLink Interface Endpoint for S3: Primarily used for accessing AWS services over a private connection but is more complex and costly compared to a gateway endpoint for S3.

Multiple NAT Gateways: Increases costs significantly and adds complexity without offering the cost benefits of a gateway endpoint.

Best Solution:

Gateway Endpoint for S3: This solution provides the required access with the least data transfer costs and minimal complexity.

Requirement Analysis: The goal is to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL with minimal application code changes.

Babelfish for Aurora PostgreSQL: Babelfish allows Aurora PostgreSQL to understand SQL Server queries natively, reducing the need for application code changes.

AWS Schema Conversion Tool (SCT): This tool helps in converting the database schema from SQL Server to PostgreSQL.

AWS Database Migration Service (DMS): DMS can be used to migrate data from SQL Server to Aurora PostgreSQL seamlessly.

Combined Approach: Enabling Babelfish addresses the SQL query compatibility, while SCT and DMS handle the schema and data migration.

References

Babelfish for Aurora PostgreSQL:Babelfish Documentation

AWS SCT and DMS:AWS Database Migration Service

Requirement Analysis: The application needs near real-time access to data, persistence, and minimal operational overhead.

ElastiCache for Redis: Provides in-memory data storage with persistence, supporting fast access and durability.

Operational Overhead: Managed service reduces the burden of setup, maintenance, and scaling.

Implementation:

Deploy an ElastiCache for Redis cluster.

Configure Redis to persist data to disk using AOF (Append-Only File) or RDB (Redis Database Backup) snapshots.

Conclusion: ElastiCache for Redis meets the requirements for fast access, data persistence, and low operational overhead.

References

Amazon ElastiCache:ElastiCache for Redis Documentation

Understanding the Requirement: The company needs to automate inventory and updates of diverse OS versions on EC2 instances and summarize common vulnerabilities for monthly reviews.

Analysis of Options:

Systems Manager Patch Manager and Security Hub: Patch Manager automates patching, but Security Hub is more focused on compliance and security posture rather than inventory and vulnerability management.

Systems Manager Patch Manager and Amazon Inspector: Patch Manager automates OS updates, and Amazon Inspector provides vulnerability assessments, making this a comprehensive solution for the requirements.

AWS Shield Advanced and AWS Config: Shield Advanced is for DDoS protection, not suitable for OS patch management and vulnerability reporting.

Amazon GuardDuty and AWS Config: GuardDuty is for threat detection and monitoring, not specifically for patch management and vulnerability assessments.

Best Solution:

Systems Manager Patch Manager and Amazon Inspector: This combination automates OS updates and provides detailed vulnerability assessments, meeting both the inventory and security reporting needs effectively.

Understanding the Requirement: The company needs to process images as they are uploaded to S3 in a cost-effective manner, currently using an EC2 Spot Fleet for nightly processing.

Analysis of Options:

S3 Event Notifications to SQS and Lambda: This setup allows for event-driven processing with Lambda, which scales automatically based on the number of messages in the queue. It is cost-effective as Lambda charges are based on the compute time used.

S3 Event Notifications to SQS and EC2 Reserved Instance: Involves managing EC2 instances, which adds operational overhead and is less cost-effective.

S3 Event Notifications to SNS and ECS: More complex and potentially less cost-effective compared to using Lambda for simple processing tasks.

S3 Event Notifications to SNS: Requires additional configuration and management to process messages.

Best Solution:

S3 Event Notifications to SQS and Lambda: This option is the most cost-effective and scalable, leveraging AWS managed services with minimal operational overhead.

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.

Requirement Analysis: The goal is to prevent accidental deletion of EBS snapshots while avoiding indefinite retention.

Recycle Bin for EBS Snapshots: AWS Recycle Bin allows for retention rules that prevent immediate deletion of snapshots, providing a safety net against accidental deletions.

Retention Rule: A 7-day retention rule ensures snapshots are not permanently deleted immediately, giving time to recover from accidental deletions.

Implementation:

Enable Recycle Bin in your AWS account.

Create a retention rule that specifies a 7-day period for EBS snapshots.

Apply this rule to all EBS snapshots.

Conclusion: This solution provides an automated way to prevent data loss from accidental deletions with minimal development effort.

References

AWS Recycle Bin:AWS Recycle Bin Documentation

Understanding the Requirement: The company needs to collect transaction data in real time, avoid data duplication, and perform additional processing without managing infrastructure.

Analysis of Options:

SQS FIFO Queue with Lambda: Ensures data is processed in order and prevents duplication.Lambda handles processing without the need to manage servers.

SQS FIFO Queue with AWS Batch: While this ensures no duplicates, it introduces additional complexity and management overhead with AWS Batch.

Kinesis Data Streams with AWS Batch and EC2: Involves more components and infrastructure management, which is against the requirement of not wanting to manage infrastructure.

Step Functions with Lambda and EC2: Involves setting up multiple services and still requires managing EC2 instances, increasing complexity.

Best Solution:

SQS FIFO Queue with Lambda: This combination ensures real-time data processing, prevents duplication, and minimizes infrastructure management, meeting all requirements efficiently.

Understanding the Requirement: The order processing system requires multiple independent validation steps, each handled by separate Lambda functions, with each function accessing only the subset of order information it needs. The system should be loosely coupled to accommodate future changes.

Analysis of Options:

Amazon SQS with a new Lambda function for transformation: This involves additional complexity in creating and managing multiple SQS queues and an extra Lambda function for data transformation.

Amazon SNS with message filtering: While SNS supports message filtering, it is more suited for pub/sub messaging patterns rather than event-driven processing requiring fine-grained control over the data sent to each function.

Amazon EventBridge with input transformers: EventBridge is designed for event-driven architectures, allowing for fine-grained control with input transformers that can modify and filter the event data sent to each target Lambda function, ensuring each function receives only the necessary information.

SQS with synchronous Lambda invocations: This approach adds unnecessary complexity with synchronous invocations and is not ideal for an event-driven, loosely coupled architecture.

Best Solution:

Amazon EventBridge with input transformers: This option provides the most flexible, scalable, and loosely coupled architecture, enabling each Lambda function to receive only the required subset of data.

Understanding the Requirement: The company needs to migrate applications to AWS, maintaining isolated networks while allowing communication with a shared services VPC and among the applications.

Analysis of Options:

Software VPN Tunnels: This approach involves high administrative overhead and complexity in managing multiple VPN connections.

VPC Peering: While suitable for smaller numbers of VPCs, it becomes complex and hard to manage at scale with over 100 applications.

Direct Connect: Primarily used for high-bandwidth, low-latency connections to on-premises networks, not inter-VPC communication.

Transit Gateway: Simplifies network management by acting as a central hub, allowing easy routing and scalability as more applications are migrated.

Best Solution:

Transit Gateway: This provides a scalable, efficient solution with minimal administrative overhead for managing network connections between multiple VPCs and the shared services VPC.

Understanding the Requirement: The application experiences slow performance at the start of peak hours, but normalizes after a few hours. The goal is to ensure proper performance at the beginning of peak hours.

Analysis of Options:

Application Load Balancer: Ensures proper traffic distribution but does not address the need to have sufficient instances running at the start of peak hours.

Dynamic Scaling Policy Based on Memory or CPU Utilization: While dynamic scaling reacts to usage metrics, it may not preemptively scale in anticipation of peak hours, leading to delays as new instances are launched and become available.

Scheduled Scaling Policy: This allows the Auto Scaling group to launch instances ahead of time, ensuring that enough instances are available and ready to handle the increased load right at the start of peak hours.

Best Solution:

Scheduled Scaling Policy: This approach ensures that new instances are launched and ready before peak hours begin, addressing the slow performance issue at the start of peak periods.

Understanding the Requirement: The company wants to cost-optimize their workload that uses EC2, Lambda, Fargate, and SageMaker, covering the most services with the fewest savings plans.

Analysis of Options:

EC2 Instance Savings Plan: Limited to EC2 and SageMaker, missing coverage for Lambda and Fargate.

Compute Savings Plan: Provides the most flexibility, covering a broad range of compute services, including EC2, Lambda, Fargate, and SageMaker.

SageMaker Savings Plan: Specifically for SageMaker, missing coverage for EC2, Lambda, and Fargate.

Combination of plans: The Compute Savings Plan is versatile and can be combined to cover different services efficiently.

Best Solution:

Compute Savings Plan for EC2, Lambda, and SageMaker: Covers the primary compute services efficiently.

Compute Savings Plan for Lambda, Fargate, and EC2: Covers the remaining services, ensuring broad coverage with minimal plans.

This answer is correct because it meets the requirements of optimizing cost and reducing the workload on the database. Amazon CloudFront is a content delivery network (CDN) service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users. CloudFront delivers your content through a worldwide network of data centers called edge locations. When a user requests content that you’re serving with CloudFront, the request is routed to the edge location that providesthe lowest latency (time delay), so that content is delivered with the best possible performance. You can create an Amazon CloudFront distribution to host the static web contents from an Amazon S3 bucket, which is an origin that you define for CloudFront. This way, you can offload the requests for static web content from your EC2 instances to CloudFront, which can improve the performance and availability of your website, and reduce the cost of running your EC2 instances.

it allows the company to encrypt the Kubernetes secrets object in the EKS cluster with the least operational overhead. By enabling secrets encryption in the EKS cluster, the company can use AWS Key Management Service (AWS KMS) to generate and manage encryption keys for encrypting and decrypting secrets at rest. This is a simple and secure way to protect sensitive information in EKS clusters. References:

Encrypting Kubernetes secrets with AWS KMS

Kubernetes Secrets

This solution meets the requirements for a highly available application with web, application, and database tiers, as well as providing edge-based content delivery. Additionally, it maximizes security by having the ALB in a private subnet, which limits direct access to the web servers, while still being able to serve traffic over the Internet via the public ALB. This will ensure that the web servers are not exposed to the public Internet, which reduces the attack surface and provides a secure way to access the application.

Amazon DynamoDB Accelerator (DAX) is a fully managed in-memory cache for DynamoDB that improves the performance of DynamoDB tables by up to 10 times and provides microsecond level of response time at any scale. It is compatible with DynamoDB API operations and requiresminimal code changes to use1. By configuring DAX for the new messages table, the solution can reduce the latency for reading new messages with minimal application changes.

B. Add DynamoDB read repticas to handle the increased read load. Update the application to point to the read endpoint for the read replicas. This solution will not work, as DynamoDB does not support read replicas as a feature. Read replicas are available for Amazon RDS, not for DynamoDB2.

C. Double the number of read capacity units for the new messages table in DynamoDB. Continue to use the existing DynamoDB endpoint. This solution will not meet the requirement ofreading new messages with aslittle latency as possible, as increasing the read capacity units will only increase the throughput of DynamoDB, not the performance or latency3.

D. Add an Amazon ElastiCache for Redis cache to the application stack. Update the application to point to the Redis cache endpoint instead of DynamoDB. This solution will not meet the requirement of minimal application changes, as adding ElastiCache for Redis will require significant code changes to implement caching logic, such as querying cache first, updating cache after writing to DynamoDB, and invalidating cache when needed.

Reference URL:

AWS Backup is a fully managed service that allows you to centralize and automate data protection of AWS services across compute, storage, and database. AWS Backup Vault Lock is an optional feature of a backup vault that can help you enhance the security and control over your backup vaults. When a lock is active in Compliance mode and the grace time is over, the vault configuration cannot be altered or deleted by a customer, account/data owner, or AWS. This ensures that your backups are available for you until they reach the expiration of their retention periods and meet the regulatory

To migrate a MySQL database to AWS with compatibility and scalability, Amazon Aurora is a suitable option. Aurora is compatible with MySQL and can scale automatically with Aurora Auto Scaling. AWS Database Migration Service (AWS DMS) can be used to migrate the database from on-premises to Aurora with minimal downtime.

 it allows the company to store sensitive customer information in a managed AWS service and give customers the ability to complete purchase transactions on the website. By using AWS Key Management Service (AWS KMS) client-side encryption, the company can encrypt the data before sending it to Amazon RDS for MySQL. This ensures that sensitive customer data is protected, even from database administrators, as only the application has access to the encryption keys. References:

Using Encryption with Amazon RDS for MySQL

Encrypting Amazon RDS Resources

AWS Backup is a fully managed service that enables users to centralize and automate the backup of data across AWS services. It can create and manage backup plans that specify the frequency and retention period of backups. It can also assign backup resources to backup vaults, which are containers that store backup data1. By using AWS Backup, the solution can ensure that the RDS backups are consistent, restorable, and retained for a minimum period of 2 years.

B. Configure a backup window for the RDS DB instances for daily snapshots. Assign a snapshot retention policy of 2 years to each RDS DB instance. Use Amazon Data Lifecycle Manager (Amazon DLM) to schedule snapshot deletions. This solution will not meet the requirement of ensuring that the backupsare consistent and restorable, as Amazon DLM is not compatible with RDS snapshots and cannot be used to schedule snapshot deletions2.

C. Configure database transaction logs to be automatically backed up to Amazon CloudWatch Logs with an expiration period of 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as database transaction logs are not sufficient to restore a database to a point in time. They only capture the changes made to the database, not the full state of the database3.

D. Configure an AWS Database Migration Service (AWS DMS) replication task. Deploy a replication instance, and configure a change data capture (CDC) task to stream database changes to Amazon S3 as the target. Configure S3 Lifecycle policies to delete the snapshots after 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as AWS DMS is a service that helps users migrate databases to AWS, not back up databases. It also requires additional resources and configuration, such as replication instances and CDC tasks.

Reference URL:

 it allows the EC2 instances to read and write rapidly and concurrently to shared storage across two Availability Zones. Amazon EFS provides a scalable, elastic, and highly available file system that can be mounted from multiple EC2 instances. Amazon EFS supports high levels of throughput and IOPS, and consistent low latencies. Amazon EFS also supports NFSv4 lock upgrading and downgrading, which enables high levels of concurrency. References:

Amazon EFS Features

Using Amazon EFS with Amazon EC2

AWS Transit Gateway is a highly scalable and centralized hub for connecting multiple VPCs, on-premises networks, and remote networks. It simplifies network connectivity by providing a single entry point and reducing the number of connections required. In this scenario, deploying an AWS Transit Gateway in the networking team's AWS account allows for efficient management and control over the network connectivity across multiple VPCs.

To ingest and process high-volume streaming data with the least operational overhead, Amazon Kinesis Data Firehose is a suitable solution. Amazon Kinesis Data Firehose can capture, transform, anddeliver streaming data to Amazon S3 or other destinations. Amazon Kinesis Data Firehose can scale automatically to match the throughput of the data and handle any amount of data. Amazon Kinesis Data Firehose is also a fully managed service that does not require any servers to provision or manage.

This answer is correct because it reflects the effect of the IAM policy on the group members. The policy has two statements: one with an Allow effect and one with a Deny effect. The Allow statement grants permission to perform any EC2 action on any resource within the us-east-1 Region. The Deny statement overrides the Allow statement and denies permission to perform the ec2:StopInstances and ec2:TerminateInstances actions on any resource within the us-east-1 Region, unless the group member is logged in with MFA. Therefore, the group members can perform any EC2 action except stopping or terminating instances in the us-east-1 Region, unless they use MFA.

The number of instances in your Auto Scaling group can be driven by how long it takes to process a message and the acceptable amount of latency (queue delay). The solution is to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain.

Using RDS Proxy, you can handle unpredictable surges in database traffic. Otherwise, these surges might cause issues due to oversubscribing connections or creating new connections at a fast rate. RDS Proxy establishes a database connection pool and reuses connections in this pool. This approach avoids the memory and CPU overhead of opening a new database connection each time. To protect the database against oversubscription, you can control the number of database connections that are

You can use metric streams to continually stream CloudWatch metrics to a destination of your choice, with near-real-time delivery and low latency. One of the use cases is Data Lake: create a metric stream and direct it to an Amazon Kinesis Data Firehose delivery stream that delivers your CloudWatch metrics to a data lake such as Amazon

To allow the MySQL database in the private subnets to access the internet without exposing it to the public, a NAT gateway is a suitable solution. A NAT gateway enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances. A NAT gateway resides in the public subnets and can handle high throughput of traffic with low latency. A NAT gateway is also a managed service that does not require any operational overhead.

Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon Relational Database Service (RDS) that makes applications more scalable, more resilient to database failures, and more secure1. RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability2. RDS Proxy also reduces failover times for Aurora and RDS databases by up to66% and enables IAM authentication and Secrets Manager integration for database access1. RDS Proxy can be enabled for most applications with no code changes2.

This answer is correct because it meets the requirements of scaling to meet future application capacity demands and ensuring high availability across all three Availability Zones. By migrating the MySQL database to Amazon RDS for MySQL with a Multi-AZ DB cluster deployment, the company can benefit from automatic failover, backup, and patching of the database across multiple Availability Zones. By using Amazon ElastiCache for Redis with high availability, the company can store session data and cache reads in a fast, in-memory data store that can also fail over across Availability Zones. By migrating the web server to an Auto Scaling group that is in three Availability Zones, the company can automatically scale the web server capacity based on the demand and traffic patterns.

Two-Way Messaging: Receive SMS messages from your customers and reply back to them in a chat-like interactive experience. With Amazon Pinpoint, you can create automatic responses when customers send you messages that contain certain keywords. You can even use Amazon Lex to create conversational bots. A majority of mobile phone users read incoming SMS messages almost immediately after receiving them. If you need to be able to provide your customers with urgent or important information, SMS messaging may be the right solution for you. You can use Amazon Pinpoint to create targeted groups of customers, and then send them campaign-based messages. You can also use Amazon Pinpoint to send direct messages, such as appointment confirmations, order updates, and one-time passwords.

S3 Intelligent-Tiering is a storage class that automatically reduces storage costs by moving data to the most cost-effective access tier based on access frequency. It has two access tiers: frequent access and infrequent access. Data is stored in the frequent access tier by default, and moved to the infrequent access tier after 30 consecutive days of no access. If the data is accessed again, it is moved back to the frequent access tier1. By using S3 Lifecycle rules to transition objects from S3 Standard to S3 Intelligent-Tiering, the solution can reduce S3 costs for data with unknown or changing access patterns.

A. Use S3 replication to transition infrequently accessed objects to S3 Standard-Infrequent Access (S3 Standard-IA). This solution will not meet the requirement of reducing S3 costs for data with unknown or changing access patterns, as S3 replication is a feature that copies objects across buckets or Regions for redundancy or compliance purposes. It does not automaticallymove objects to a different storage class based on access frequency2.

B. Use S3 Lifecycle rules to transition objects from S3 Standard to Standard-Infrequent Access (S3 Standard-IA). This solution will not meet the requirement of reducing S3 costs for data with unknown or changing access patterns, as S3 Standard-IA is a storage class that offers lowerstorage costs than S3 Standard, but charges a retrieval fee for accessing the data. It is suitable for long-lived and infrequently accessed data, not for data with changing access patterns1.

D. Use S3 Inventory to identify and transition objects that have not been accessed from S3 Stand-ard to S3 Intelligent-Tiering. This solution will not meet the requirement of reducingS3 costs for data with unknown or changing access patterns, as S3 Inventory is a feature that provides a report of the objects in a bucket and their metadata on a daily or weekly basis. It does not automatically move objects to a different storage class based on access frequency3.

Reference URL:

S3 Intelligent-Tiering is the best solution for reducing S3 costs when the access pattern is unpredictable or changing. S3 Intelligent-Tiering automatically moves objects between two access tiers (frequent and infrequent) based on the access frequency, without any performance impact or retrieval fees. S3 Intelligent-Tiering also has an optional archive tier for objects that are rarely accessed. S3 Lifecycle rules can be used to transition objects from S3 Standard to S3 Intelligent-Tiering.

Reference URLs:

AWS Global Accelerator is a networking service that improves the performance and availability of applications for global users. It uses the AWS global network to route user traffic to the optimal endpoint based on performance and health. It also provides static IP addresses that act as a fixed entry point to the applications and support both TCP and UDP protocols1. By using AWS Global Accelerator, the solution can ensure the lowest possible latency for all users.

A. Use AWS Global Accelerator to create an accelerator. Create an Application Load Balancer (ALB) behind an accelerator endpoint that uses Global Accelerator integration and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. This solution will not work, as ALB does not support UDP protocol2.

C. Create an Amazon CloudFront content delivery network (CDN) endpoint. Create a Network Load Balancer (NLB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the NLB. Update CloudFront to use the NLB as the origin. This solution will not work, as CloudFront does not support UDP protocol3.

D. Create an Amazon Cloudfront content delivery network (CDN) endpoint. Create an Application Load Balancer (ALB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. Update CloudFront to use the ALB as the origin. This solution will not work, as CloudFront and ALB do not support UDP protocol23.

Reference URL:

Dynamic scaling is a type of autoscaling that automatically adjusts the number of EC2 instances in an Auto Scaling group based on demand or load. It uses CloudWatch alarms to trigger scaling actions when a specified metric crosses a threshold. It can scale out (addinstances) or scale in (remove instances) as needed1. By using dynamic scaling, the solution can maintain application performance during sudden traffic increases most cost-effectively.

A. Use manual scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as manual scaling requires users to manually increase or decrease the number of instances through a CLI or console. It does not respond automatically to changes in demand or load2.

B. Use predictive scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of most cost-effectiveness, as predictive scaling uses machine learning and artificial intelligence tools to evaluate traffic loads and anticipate when more or fewer resources are needed. It performs scheduled scaling actions based on the prediction, which may not matchthe actual demand or load at any given time. Predictive scaling is more suitable for scenarios where there are predictable traffic patterns or known changes in traffic loads3.

D. Use schedule scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as schedule scaling performs scaling actions at specific times that users schedule. It does not respond automatically to changes in demand or load. Schedule scaling is more suitable for scenarios where there are predictable traffic drops or spikes at specific times of the day.

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AWS uses the existing infrastructure of a VPC to create a VPC peering connection; it is neither a gateway nor a VPN connection, and does not rely on a separate piece of physical hardware. There is no single point of failure for communication or a bandwidth

The Kubernetes Cluster Autoscaler is a component that automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled onto other nodes. It uses Auto Scaling groups to scale up or down the nodes according to the demand and capacity ofyour cluster1.

By using the Kubernetes Cluster Autoscaler in your Amazon EKS cluster, you can achieve the following benefits:

You can improve the performance and availability of your container applications by ensuring that there are enough nodes to run your pods and that there are no idle nodes wasting resources.

You can reduce the administrative overhead of managing your cluster size manually or using custom scripts. The Cluster Autoscaler handles the scaling decisions and actions for you based on the metrics and events from your cluster.

You can leverage the integration of Amazon EKS and AWS Auto Scaling to optimize the cost and efficiency of your cluster. You can use features such as launchtemplates, mixed instances policies, and spot instances to customize your node configuration and save up to 90% on compute costs2

Joining the FSx for Windows File Server file system to the on-premises Active Directory will allow the company to use the existing Active Directory groups to restrict access to the file shares, folders, and files after the move to AWS. This option allows the company to continue using their existing access controls and management structure, making the transition to AWS more seamless.

S3 Storage Lens is a cloud storage analytics feature that provides organization-wide visibility into object storage usage and activity across multiple AWS accounts in AWS Organizations. S3 Storage Lens can report the incomplete multipart upload object count as one of the metrics that it collects and displays on an interactive dashboard in the S3 console. S3 Storage Lens can also export metrics in CSV or Parquet format to an S3 bucket for further analysis. This solution will meet the requirements with the least operational overhead, as it does not require any code development or policy changes.

To provide individual and secure URLs for all customers using an API Gateway REST API, you need to do the following steps:

A. Register the required domain in a registrar. Create a wildcard custom domain name in a Route 53 hosted zone and record in the zone that points to the API Gateway endpoint. This step will allow you to use a custom domain name for your API instead of the default one generated by API Gateway. A wildcard custom domain name means that you can use any subdomain under your domain name (such as customer1.example.com or customer2.example.com) to access your API. You need to register your domain name with a registrar (such as Route 53 or a third-party registrar) and create a hosted zone in Route 53 for your domain name. You also need to create a record in the hosted zone that points to the API Gateway endpoint using an alias record.

D. Request a wildcard certificate that matches the custom domain name in AWS Certificate Manager (ACM) in the same Region. This step will allow you to secure your API with HTTPS using a certificate issued by ACM. A wildcard certificate means that it can match any subdomain under your domain name (such as *.example.com). You need to request or import a certificate in ACM that matches your custom domain name and verify that you own the domain name. You also need to request the certificate in the same Region as your API.

F. Create a custom domain name in API Gateway for the REST API. Import the certificate from AWS Certificate Manager (ACM). This step will allow you to associate your custom domain name with your API and use the certificate fromACM to enable HTTPS. You need to create a custom domain name in API Gateway for the REST API and specify the certificate ARN from ACM. You also need to create a base path mapping that maps a path from your custom domain name to your API stage.

This answer is correct because it meets the requirements of releasing the new version of APIs with minimal effects on customers and minimal data loss. A canary release deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage. In a canary release deployment, total API traffic is separated at random into a production release and a canary release with a pre-configured ratio. Typically, the canary release receives a small percentage of API traffic and the production release takes up the rest. The updated API features are only visible to API traffic through the canary. You can adjust the canary traffic percentage to optimize test coverage or performance. By keeping canary traffic small and the selection random, most users are not adversely affected at any time by potential bugs in the new version, and no single user is adversely affected all the time. After the test metrics pass your requirements, you can promote the canary release to the production release and disable the canary from the deployment. This makes the new features available in the production stage.

By using Amazon S3 and AWS Lambda together, you can create a serverless architecture that provides highly scalable and available image resizing capabilities. Here's how the solution would work: Set up an Amazon S3 bucket to store the original images uploaded by users. Configure an event trigger on the S3 bucket to invoke an AWS Lambda function whenever a new image is uploaded. The Lambda function can be designed to retrieve the uploaded image, perform the necessary resizing operations based on device requirements, and store the resized images back in the S3 bucket or a different bucket designated for resized images. Configure the Amazon S3 bucket to make the resized images publicly accessible for serving to users.

A cross-account IAM role is a way to grant users from one AWS account access to resources in another AWS account. The cross-account IAM role can have a read-only access policy attached to it, which allows the users to download objects from the S3 buckets without modifying or deleting them. The cross-account IAM role also reduces the operational overhead of managing multiple IAM users and policies in each account. The cross-account IAM role meets all the requirements of the question, while the other options do not. References:

Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda scales automatically based on the incoming requests, but it may take some time to initialize new instances of your function if there is a sudden increase in demand. This may result in high latency or cold starts for your API. To avoid this, you can use provisioned concurrency, which ensures that your function is initialized and ready to respond at any time. Provisioned concurrency also helps you achieve consistent low latency for your API by reducing the impact of scaling on performance.

Workload Discovery on AWS (formerly called AWS Perspective) is a tool that visualizes AWS Cloud workloads. It maintains an inventory of the AWS resources across your accounts and Regions, mapping relationships between them, and displaying them in a web UI. It also allows you to query AWS Cost and Usage Reports, search for resources, save and export architecture diagrams, and more1. By using Workload Discovery on AWS, the solution can build and map the relationship details of the various workloads across all accounts with the least operational effort.

A. Use AWS Systems Manager Inventory to generate a map view from the detailed view report. This solution will not meet the requirement of building and mapping the relationship details of the various workloads across all accounts, as AWS Systems Manager Inventory is a feature thatcollects metadata from your managed instances and stores it in a central Amazon S3 bucket. It does not provide a map view or architecture diagrams of the workloads2.

B. Use AWS Step Functions to collect workload details Build architecture diagrams of the work-loads manually. This solution will not meet the requirement of the least operational effort, as it involves creating and managing state machines to orchestrate the workload details collection, and building architecture diagrams manually.

D. Use AWS X-Ray to view the workload details Build architecture diagrams with relationships. This solution will not meet the requirement of the least operational effort, as it involves instrumenting your applications with X-Ray SDKs to collect workload details, and building architecture diagrams manually.

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it allows CloudFormation to access the template in the S3 bucket without granting public access or creating additional resources. A presigned URL is a URL that is signed with the access key of an IAM user or role that has permission to access the object. The presigned URL can be used by anyone who receives it, but it expires after a specified time. By creating a presigned URL for the template object and configuring the CloudFormation stack to use it, the company can grant CloudFormation access to the template based on specific user requests and follow security best practices. References:

Using Amazon S3 Presigned URLs

Using Amazon S3 Buckets

An Auto Scaling group is a collection of EC2 instances that share similar characteristics and can be scaled in or out automatically based on demand. An Auto Scaling group can be placed behind an Application Load Balancer, which is a type of Elastic Load Balancing load balancer that distributes incoming traffic across multiple targets in multiple Availability Zones. This solution will improve the resiliency of the web tier by providing high availability, scalability, and fault tolerance. An Amazon RDS Multi-AZ deployment is a configuration that automatically creates a primary database instance and synchronously replicates the data to a standby instance in a different Availability Zone. When a failure occurs, Amazon RDS automatically fails over to the standby instance without manual intervention. This solution will improve the resiliency of the database tier by providing data redundancy, backup support, and availability. This combination of steps will meet the requirements with minimal changes to the application during the migration.

AWS Organizations is a service that helps users centrally manage and govern multiple AWS accounts. It allows users to create organizational units (OUs) to group accounts based on business needs or other criteria. It also allows users to define and attach service control policies (SCPs) to OUs or accounts to restrict the actions that can be performed by the accounts1. By creating a new organization in AWS Organizations with all features turned on, the solution can consolidate and manage the new AWS accounts for different business units.

AWS IAM Identity Center (formerly known as AWS Single Sign-On) is a service that provides single sign-on access for all of your AWS accounts and cloud applications. It connects with Microsoft Active Directory through AWS Directory Service to allow users in that directory to sign in to a personalized AWS access portal using their existing Active Directory user names and passwords. From the AWS access portal, users have access to all the AWS accounts and cloudapplications that they have permissions for2. By setting up IAM Identity Center in the organization and integrating it with the company’s corporate directory service, the solution can authenticate access to these AWS accounts using a centralized corporate directory service.

B. Set up an Amazon Cognito identity pool. Configure AWS IAM Identity Center (AWS Single Sign-On) to accept Amazon Cognito authentication. This solution will not meet the requirement of authenticating access to these AWS accounts by using a centralized corporate directory service, as Amazon Cognito is a service that provides user sign-up,sign-in, and access control for web and mobile applications, not for corporate directory services3.

C. Configure a service control policy (SCP) to manage the AWS accounts. Add AWS IAM Identi-ty Center (AWS Single Sign-On) to AWS Directory Service. This solution will not work, as SCPs are used to restrict the actions that can be performed by the accounts in an organization, not to manage theaccounts themselves1. Also, IAM Identity Center cannot be added to AWS Directory Service, as it is a separate service that connects with Microsoft Active Directory through AWS Directory Service2.

D. Create a new organization in AWS Organizations. Configure the organization’s authentication mechanism to use AWS Directory Service directly. This solution will not work, as AWS Organizations does not have an authentication mechanism that can use AWS Directory Service directly. AWS Organizations relies on IAM Identity Center to provide single sign-on access for the accounts in an organization.

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User-defined tags are key-value pairs that can be applied to AWS resources to categorize and track them. User-defined tags can also be used to allocate costs and create detailed billing reports in the AWS Billing console. To use user-defined tags for cost allocation, the tags must be activated from the Organizations management account, which is the root account that has fullcontrol over all the member accounts in the organization. Once activated, the user-defined tags will appear as columns in the cost allocation report, and can be used to filter and group costs by product line. This solution will meet the requirements with the least operational overhead, as it leverages the existing tagging strategy and does not require any code development or manual intervention.

it allows the company to store and deliver images to users in a highly available and cost-effective way. By storing images in Amazon S3 Standard, the company can use a durable, scalable, and secure object storage service that offers high availability and performance. By using S3 Standard to directly deliver images by using a static website, the company can avoid running web servers and reduce operational overhead. S3 Standard also offers low storage pricing and free data transfer within AWS Regions. References:

Amazon S3 Storage Classes

Hosting a Static Website on Amazon S3

Using S3 gateway endpoints allows private and cost-free access to S3 without routing traffic through a NAT gateway. NAT gateway traffic incurs charges, especially when used across multiple Availability Zones.

By using an S3 gateway endpoint, EC2 instances in private subnets can access S3 directly without needing internet access, reducing both data transfer and NAT gateway costs.

Interface endpoints are more expensive and typically used for services like API Gateway or Systems Manager.

AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.

Amazon RDS Proxy helps manage thousands of concurrent database connections by pooling and reusing them efficiently. It is especially useful for serverless applications like AWS Lambda that can open numerous connections quickly, potentially overwhelming the database. Using RDS Proxy reduces connection management overhead and improves fault tolerance.