Salesforce Data-Architect Salesforce Certified Data Architect (SP25) Exam Practice Test

According to the official Salesforce guide1, data classification metadata is a feature that allows administrators to classify data fields based on their sensitivity level, such as confidential, restricted, or general. Data classification metadata can be used to filter and identify sensitive data fields and apply appropriate security measures, such as encryption, masking, or auditing. Option D is the correct answer because it suggests using data classification metadata to provide ways to filter and identify sensitive data. Option A is incorrect because data grouping metadata is not a feature in Salesforce. Option B is incorrect because field-level security is a feature that controls the visibility and editability of fields based on user profiles or permission sets, but it does not provide ways to filter and identify sensitive data. Option C is incorrect because creating a custom checkbox denoting sensitive data is not a scalable or reliable solution, as it requires manual maintenance and does not enforce any security measures.

 Creating a duplicate rule for account, lead, and contact, using standard matching rules for these objects, and setting the action to report and alert for both creates and edits can help UC fix the issue immediately by prompting users about a record that possibly exists in Salesforce. This can also generate a report regarding duplicate records that can be used for further analysis and resolution

 Creating the users, uploading all data, and then deploying the sharing rules will reduce the number of sharing recalculations that occur during the data load. Deferring sharing calculations until the data has finished uploading will also improve the performance by postponing the sharing rule evaluation. These are the recommended best practices for loading large data sets into Salesforce

According to the Get Started with Custom Metadata Types unit on Trailhead, one of the use cases for custom metadata types is to define custom charges for an accounting app. The unit states that “Say that your org uses a standard accounting app. You can create a custom metadata type that defines custom charges, like duties and VAT rates. Then you can write some Apex code that calculates the total amount due for each invoice by using the metadata from your custom metadata type.” Therefore, a similar approach can be used to maintain the pricing criteria for a custom pricing engine in Salesforce.

Contacting Salesforce to enable Defer Sharing Rules is the feature that would ensure that performance, when moving Sales Reps between regions, remains adequate while meeting existing requirements. Defer Sharing Rules allows you to defer sharing rule recalculation when you make changes to public groups or roles that are used in sharing rules. This can improve performance and avoid locking issues when you move users between groups or roles2. Implementing data archiving for old Shipment records will not help with the performance issue related to sharing rules. Contacting Salesforce to create Skinny tables on Shipment will not help with the performance issue related to sharing rules. Configuring shipment OWD to Public Read/Write will not meet the existing requirements of having a Private sharing model.

The recommended solution for the Cloud Kicks Data Architect to address the requirements is to create a custom object to track shipment requests and a child custom object to track shipment items. Implement an archiving process that moves data off-platform after three years. This solution would allow Cloud Kicks to store and manage their shipment data on Salesforce, and use Salesforce reports to analyze it. However, since Cloud Kicks expects a large volume of data over time, they should implement an archiving process that moves data off-platform after three years to avoid hitting the Org data storage limit and maintain optimal performance3. External objects are not a good option for this scenario, because they are stored off-platform in an external system, such as Heroku’s Postgres database, and they have limited functionality and performance compared to custom objects

A required Lookup relationship ensures that the Shipment record must have a value for each of the three parent records, and also prevents the deletion of those parent records if they are referenced by a Shipment record. A Master-Detail relationship would not allow separate sharing models for each custom object, and a VLOOKUP formula field would not enforce the relationship or prevent deletion

The best design pattern to satisfy the requirement of accessing order data from an on-premise database with REST API access without consuming SF storage limits is to use SF Connect to virtualize the data in SF and avoid storage limits. SF Connect is an integration tool that allows users to access and integrate data from external sources using external objects. External objects are similar to custom objects, except that the data resides in another system and is accessed in real time via web service callouts. SF Connect supports various adapters to connect to different types of external data sources, such as OData, cross-org, or Apex custom adapter11. Migrate and persist the data in SF to take advantage of native functionality is not a good option because it would consume SF storage limits and require data synchronization between systems. Develop a bidirectional integration between the on-premise system and Salesforce is not a good option because it would be complex and costly to implement and maintain, and it would also consume SF storage limits. Build a UI for the on-premise system and iframe it in Salesforce is not a good option because it would not provide a seamless user experience and it would not allow users to search, report, or perform actions on the external data.

The best solution to comply with the DR policy is to leverage a 3rd party tool that extract Salesforce data/metadata and stores the information in an external protected system. This solution can help create a backup of Salesforce data and metadata in case of a disaster or data loss event. It can also help restore data from the backup system to Salesforce if needed. There are various 3rd party tools available in the AppExchange or online that offer data backup and recovery services for Salesforce67. Leverage Salesforce weekly exports and store data in flat files on a protected system is not a good solution because it does not include metadata backup and it does not allow granular or automated data recovery. Utilize an ETL tool to migrate data to an on-premise archive solution is not a good solution because it does not include metadata backup and it may require complex data transformations and synchronizations. Write a custom batch job to extract data changes nightly and store in an external protected system is not a good solution because it does not include metadata backup and it may have performance or reliability issues.

Using scheduled batch Apex to copy aggregate information into a custom object and delete the original records can improve the performance and reduce the data storage limits by removing unnecessary data and keeping only the summary data that is needed for reporting. Configuring the Salesforce Archiving feature to archive older records and remove them from the data storage limits can also help with performance and storage issues by moving historical data to a separate system that is still accessible but does not affect the operational data

The recommended course of action to appropriately integrate the new Opportunity engagement system with Salesforce is to bring the stakeholders together to discuss the appropriate data strategy moving forward. This is because there may be valuable data in both systems that need to be reconciled and harmonized, and the Master Data Management (MDM) strategy may need to be revised or updated to accommodate the new system. The other options are not recommended, as they may result in data loss, inconsistency, or duplication

A data dictionary is a document that describes the structure, format, and meaning of data entities and attributes. A common technique to build a data dictionary is to use an entity relationship diagram (ERD), which shows the logical relationships between objects and fields in a graphical way. Another technique is to use the Salesforce Metadata API, which allows you to retrieve and deploy the metadata that defines your Salesforce org

The correct answer is C, create a geolocation custom field to maintain this requirement. A geolocation custom field is a compound field that can store both latitude and longitude information in a single field. It also supports geolocation functions and distance calculations. Creating formula fields or custom fields for latitude and longitude separately would be inefficient and redundant. Recommending app exchange packages would not be a direct solution to the requirement.

 Salesforce allows up to four objects in a single report type. This means that if an architect wants to create a report that compiles data from Accounts, Contacts, Opportunities, Orders, and Order Line Items, they will run into a difficulty because that would require five objects in a single report type. To overcome this limitation, the architect can either create two separate report types and join them in a joined report, or use an external reporting tool that can access data from multiple objects.

The recommended data model for consumer account data to be stored in Salesforce is to use the Account object with Person Accounts and a new B2C page layout. Person Accounts are a special type of accounts that allow you to store information about individual consumers who are not associated with a business account2. Person Accounts have the following advantages3:

They allow you to use the same standard objects and features that you use for business accounts, such as contacts, opportunities, cases, etc.

They enable you to create different page layouts and record types for B2C and B2B customers, which allows you to customize the user interface and business logic for each segment.

They support large data volumes and can scale up to 50 million records or more, which meets the expected growth of consumers over the next five years.

 Event Monitoring is a feature that allows you to track user actions, such as logins, logouts, downloads, etc., in your Salesforce org. You can use Event Monitoring to monitor performance, usage, security, and compliance

Service Cloud License (option C) is the license type that UC users need to fulfill UC’s requirements, as it allows them to track customer complaints, provide white papers on products, and provide subscription-based support. Sales Cloud License (option A) is mainly for managing sales processes and leads, Lightning Platform Starter License (option B) is for building custom apps and workflows, and Salesforce License (option D) is a generic term that does not specify a particular license type.

The data architect should recommend Lighting platform plus license type for the finance team that best meets the requirements. This license type allows users to access custom objects and run reports and dashboards on them, without accessing any sales objects. Option A is incorrect because Service Cloud license type is more suitable for customer service users who need to access cases, knowledge, and other service objects. Option B is incorrect because Sales Cloud license type is more suitable for sales users who need to access accounts, contacts, opportunities, and other sales objects. Option C is incorrect because Light Platform Start license type does not allow users to run reports and dashboards on custom objects.

Defining data duplication standards and rules, measuring data timeliness and consistency, and measuring data completeness and accuracy are three key factors that a data architect should consider while defining data quality standards. Defining data duplication standards and rules can help prevent or reduce duplicate records in the org by specifying criteria and actions for identifying and merging duplicates. Measuring data timeliness and consistency can help ensure that the data is up-to-date, reliable, and synchronized across different sources. Measuring data completeness and accuracy can help ensure that the data is sufficient, relevant, and correct for the intended purposes.

Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying “Duplicate Id Specified” and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.

The best license to provide these features to the franchisees is the Partner Community license. Partner Community licenses are designed for external users who collaborate with your sales team on deals, such as resellers, distributors, or brokers. Partner Community users can access standard CRM objects, such as accounts, contacts, leads, opportunities, campaigns, and reports. They can also access custom objects and run dashboards12. Salesforce Sales Cloud license is not a good option because it is intended for internal users who need full access to standard CRM and custom apps. Lightning Platform license is not a good option because it is intended for users who need access to custom apps but not to standard CRM functionality. Customer Community license is not a good option because it is intended for external users who need access to customer support features, such as cases and knowledge articles, but not to sales features

Option D is correct because data flows should be reviewed with the business users to determine the system of record per object or field1. The system of record is the authoritative source of data for a given entity or attribute2. It may vary depending on the business context and requirements. Option A is not correct because account and opportunity data originates in Salesforce, but it may not be the system of record for all attributes or objects if they are updated by other systems2. Option B is not correct because whatever system updates the attribute or object may not be the system of record for that field or object if there are conflicting or overlapping updates from other systems2. Option C is not correct because whatever integration data flow runs last may not determine which system is the system of record if there are different business rules or logic applied by different systems2.

 Using a third-party data source to update contact information in Salesforce is the best solution to reduce the amount of returned mails. This way, the data architect can ensure that the contact information is accurate and up-to-date without relying on manual verification or deletion of contacts

Creating an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports (option D) is the fastest and most effective way for a data architect to solve the time-out issue, as it reduces the amount of data that needs to be queried and processed by the reports. Creating custom fields on opportunity and copying data from order into those custom fields (option A) is not a good solution, as it may create data redundancy and inconsistency, and it does not address the large volume of data. Extracting opportunity and order data from Salesforce and using a third-party reporting tool (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not leverage the native reporting features of Salesforce. Creating a skinny table in Salesforce and copying order and opportunity fields into it (option C) is also not a good solution, as it may not support all the reporting requirements, and it does not reduce the number of records.

According to the exam guide, one of the objectives is to "describe best practices for implementing a single-org strategy in a B2C scenario"1. This implies that option D is the best practice for loading individual consumers as contacts in Salesforce. This approach avoids creating unnecessary accounts and reduces data duplication. Option C is not correct because it creates one account per contact, which increases data volume and complexity. Options A and B are not correct because they do not leverage the standard contact object, which provides native functionality and integration with other Salesforce features.

According to this article, Einstein Analytics for multi-org can help UC to report on accounts and opportunities from each org in one place. It can also provide advanced AI-driven analytics on the dataset using Einstein Discovery.

Implicit sharing will automatically handle this sharing requirement with standard functionality, as it grants read-only access to parent accounts when users have access to child opportunities. This is also known as account-opportunity sharing3. Creating a criteria-based sharing rule (option A) or an owner-based sharing rule (option D) is not necessary, as they are used to grant additional access based on record criteria or ownership. Adding appropriate users to the account team (option C) is also not required, as it is used to grant access to specific users or groups for individual accounts.

 Setting Customer-Reference_c as an External ID (unique) is the recommended solution to ensure that support agents using global search and reports can quickly find shipment records. This is because external ID fields are indexed and can be used as filters in reports and list views4. Making the field unique also ensures that there are no duplicate values that could confuse the search results.

The two solutions that a data architect should recommend to preserve the date timestamps and ownership on records being migrated are:

Enable update records with Inactive Owners Permission: This permission allows users to update record owner and sharing-based records with inactive owners. This can help preserve the original ownership of records that belong to users who are no longer active in Salesforce8.

Enable Set Audit fields upon Record Creation Permission: This permission allows users to set audit fields (such as Created By or Last Modified By) when they create a record via API importing tools like Data Loader. This can help preserve the original date timestamps of records that were created or modified in another system9.

Log a case with SF to update these fields is not a good solution because it is not necessary or feasible to ask Salesforce support to update these fields manually or programmatically. Enable modify all and view all permission is not a good solution because it does not affect the ability to preserve the date timestamps and ownership on records, but rather grants users access to all records regardless of sharing settings

 According to this article, migrating the legacy data leveraging an ETL tool to transform data types and load data into Salesforce can be a way to ensure successful data migrations. The article states that ETL tools can help with data cleansing, mapping, transformation, and loading, and that they can handle different data types and formats. The article also provides some best practices for data migration, such as identifying the data to migrate, creating templates for the data, preparing the destination org, and validating the data.

A correct technique UC should use to solve the data inconsistency is to define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse. A data taxonomy is a hierarchical classification of data entities and attributes that defines their meaning, format, and relationships. A data taxonomy can help ensure consistency, accuracy, and completeness of product data across different systems and channels

Enabling “Set Audit Fields” allows the user loading the data to set the value of the standard CreatedDate field to match the original date from the legacy system. This is a one-time permission that can be revoked after the migration is completed. The other options would either not work or require additional customization

Pre-processing the data means transforming and cleansing the data before loading it into Salesforce. This can reduce the errors and conflicts that might occur during the data load. Using Data Loader with SOAP API to upsert with zip compression enabled can also improve the performance and efficiency of the data load by reducing the network bandwidth and avoiding duplication

Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

Creating customers as Contacts and relating them to Business and Residential Accounts using the Account Contact Relationships (option A) is the best option to recommend for a single view of these customers without creating multiple customer records, as it allows the data architect to model complex relationships between customers and accounts using native Salesforce features and tools. Creating customers as Person Accounts and relating them to Business and Residential Accounts using the Account Contact relationship (option B) is not a good option, as it may create data redundancy and inconsistency, and it does not leverage the existing Contact object. Creating customers as individual objects and relating them with Accounts for Business and Residence accounts (option C) is also not a good option, as it may require more customization and maintenance effort, and it does not leverage the existing Account and Contact objects. Creating customers as Accounts for Residence Account and using Parent Account to relate Business Account (option D) is also not a good option, as it may create confusion and complexity with the account hierarchy, and it does not leverage the existing Contact object.

 Changing the master-detail relationship to a lookup relationship is the best solution for allowing the creation of generic discount requests without the custom product object as its master record. A lookup relationship allows you to create child records without requiring a parent record. It also gives you more flexibility in defining the sharing and security settings for each object

Doing client-side validation of phone number and email field formats and preferring picklist form fields over free text fields, where possible, are two techniques that will help maintain a high level of data quality when designing forms that create Lead records in Salesforce using the REST API. Client-side validation can ensure that the input data is in the correct format and prevent errors or invalid values from being submitted. Picklist form fields can provide a predefined set of values for the users to choose from and avoid typos or inconsistencies in the data.

Ensuring the lead data is preprocessed for quality before loading into Salesforce is the best way to maintain data quality2. Activating validation rules after the load or developing a custom Apex batch process may not catch all the errors or may require additional time and resources. Allowing validation rules to be activated during the load may cause failures or delays.

Consolidating the data from each org into a centralized datastore is the best suggestion to achieve a 360-degree view of the customer. This way, UC can have a single source of truth for all customer data and avoid data silos and inconsistencies. The other options are not feasible because they either require complex integration, additional cost, or data duplication.

Running a report that shows the last time the key fields were updated can help Get Cloudy Consulting identify stale or outdated data and prioritize data cleansing activities. Running a report that shows the percentage of blanks for the important fields can help Get Cloudy Consulting measure the completeness of their data and identify gaps or missing value

A master data management (MDM) strategy for customer data can help UC ensure that all systems have access to the same customer information, without loading or duplicating data in all systems. An MDM strategy can also help UC avoid data conflicts and inconsistencies that may arise from having multiple systems as owners of data.

Using Duplicate Management and Data.com Clean are two recommended approaches for UC to avoid duplicate data and increase the level of data quality. Duplicate Management can prevent or alert users when they try to create or edit records that are duplicates of existing records. Data.com Clean can compare Salesforce records with Data.com records and provide suggestions for updates or removals of duplicate records.

Using the Consent Management Feature to validate consent provided under the person Account and Contact that is provided by the customer (option C) is the best option to implement these requirements, as it allows UC to store and manage consent preferences for Person Accounts and Contacts using native Salesforce features and tools. Configuring custom fields in Person Account and Contact to store consent provided by customers and validate consent against the fields (option A) or building Custom object to store consent information in Person Account and Contact and validate against this object before contacting customers (option B) are not good options, as they may require more customization and maintenance effort, and they do not leverage the existing Data Protection and Privacy feature. Deleting contact information from customers who have declined consent to be contacted (option D) is also not a good option, as it may cause data loss or compliance issues, and it does not allow UC to track or update consent preferences.

 Partner community license type is the best option for building a channel sales portal, as it allows external resellers to access lead, opportunity, and order data, as well as run reports and dashboards. Customer community and customer community plus license types are more suitable for customer service portals, while lightning external apps starter license type does not support reports and dashboards

PK Chunking is a feature of the Bulk API that allows splitting a large query into smaller batches based on the primary key of the object. This can improve the performance and avoid query timeouts when querying large data sets

Einstein Analytics can provide more analytical insights than native reports and dashboards by allowing users to explore data from multiple sources, create interactive visualizations, and apply AI-powered features5. AppExchange Apps can also provide more analytical insights by offering pre-built solutions or integrations with external tools that can enhance the reporting and analytics capabilities of Salesforce6.

The correct answer is A, D, and E. The data architect should consider these three factors when evaluating the need for Salesforce Connect: You want real-time access to the latest data from other systems, you have a large amount of data that you don’t want to copy into your Salesforce org, and you need to small amounts of external data at any one time. These factors indicate that Salesforce Connect is a suitable solution for creating a connected experience in Salesforce without importing inventory data from a cloud database. Salesforce Connect allows Salesforce to access external data via OData or custom adapters without storing it in Salesforce, which reduces storage costs and ensures data freshness. Salesforce Connect also supports pagination and caching to optimize performance when accessing small amounts of external data at any one time. Option B is incorrect because if you have a large amount of data and would like to copy subsets of it into Salesforce, you may not need Salesforce Connect but rather use other tools such as Data Loader or API integration. Option C is incorrect because if you need to expose data via a virtual private connection, you may not need Salesforce Connect but rather use other tools such as VPN or VPC peering.

Using Salesforce Bulk API in parallel mode can reduce the migration time by processing multiple batches of records simultaneously and leveraging the server resources more efficiently. The Bulk API is designed for loading large amounts of data into Salesforce

Changing the opportunity amount field access to read only for sales users using field level security is the best way to address the overriding issue. This way, the sales users can still view the opportunity amount field but cannot edit it, and PPS can update it as the trusted source

The correct answer is B, data export wizard. The data export wizard is a tool that allows you to export your data in CSV format, schedule the export process to run once per week, and configure the export settings through the Salesforce UI. The data export wizard can handle up to 51 million records per export. The bulk API, third-party ETL tools, and data loader are also tools that can export data, but they are not as simple or user-friendly as the data export wizard.

 Data storage limits and API request limits are two important factors that affect the data integration and storage in Salesforce. Data storage limits determine how much data can be stored in Salesforce, and API request limits determine how many API calls can be made to Salesforce in a 24-hour period. Both of these limits depend on the edition and license type of the Salesforce org. Workflow rule limits and webservice callout limits are not directly related to data integration and storage, but rather to business logic and external services.

The three questions that should be considered when designing an appropriate archiving strategy are: If reporting is necessary, can the information be aggregated into fewer, summary records? Will the data being archived need to be reported on or accessed in any way in the future? Are there any regulatory restrictions that will influence the archiving and purging plans? These questions are important because they help determine the scope, frequency, and method of archiving and purging data from Salesforce. For example, if reporting is necessary, then summary records or analytics snapshots can be used to store aggregated data and reduce the number of records that need to be archived1. If the data being archived needs to be accessed in the future, then offsite storage or external objects can be used to retain the data and make it available on demand2. If there are any regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply with them and ensure data security and privacy

saving Apex errors in a custom object can be a way to report errors from an Apex job. The article provides an example of how to create a custom object called AsyncApexError__c and use a trigger to insert error records into it. The custom object can then be used for reporting or notification purposes.

According to Trailhead2, Customer 360 Data Manager is a feature that allows administrators to connect, reconcile, and share customer data across Salesforce orgs and external systems. Customer 360 Data Manager can sync product and pricing information from product master database to Salesforce using predefined mappings and rules, ensuring data quality and consistency. Option D is the correct answer because it suggests using Customer 360 Data Manager to sync product and pricing information from product master database to Salesforce. Option A is incorrect because building a custom integration for two-way sync of product and pricing information between product master and Salesforce can introduce data conflicts and duplication issues, as both systems can update the same data independently. Option B is incorrect because building a custom integration for one-way sync of product and pricing information from product master to Salesforce can be costly and time-consuming, as it requires custom development and maintenance. Option C is incorrect because implementing a manual process to update the products from an extract from the products master on a weekly basis can result in data latency and errors, as the products information may change more frequently than once a week.

 According to the exam guide, one of the objectives is to "describe how to use queryAll() or ALL ROWS keywords to access deleted records in Apex and SOQL"1. This implies that option C is the correct way to access deleted records in Salesforce. Option D is not correct because sending data to a data warehouse does not maintain the data in Salesforce. Options A and B are not correct because they do not apply to deleted records2.

Using leading % wildcards, performing large loads and deletions, and using NOT and != operators are three options that prevent a query from being selective. These options can cause the query to scan a large number of records or indexes, which can negatively impact the performance and scalability of the query5. Using a custom index on a deterministic formula field, on the other hand, can improve the query selectivity by allowing the query to use the index instead of scanning all the records6.

The recommended approach to re-run the test is to refresh the full copy sandbox and re-run the data migration test. A full copy sandbox is a replica of the production Org, including all data, metadata, and attachments. Refreshing a full copy sandbox means creating a new copy of the production Org and replacing the existing sandbox. This would ensure that the test is run on a clean and up-to-date environment, without any leftover data or configuration from previous tests. Truncating or hard deleting objects would not be sufficient, because they would not remove all the data or metadata from the sandbox, and they could also affect other dependent objects or processes.

To solve the issue of lack of governance in making changes to Salesforce, a data architect should recommend creating and managing a data dictionary and setting up a governance process for changes made to common objects. A data dictionary is a document that defines the metadata, structure, and relationship of each object and field in Salesforce. A governance process is a set of rules and procedures that govern how changes are proposed, reviewed, approved, and deployed in Salesforce. These measures will help NTO to maintain consistency, quality, and clarity of their data model and avoid confusion and errors among users. Option A is incorrect because adding description fields to explain why the field is used, and marking the field as required will not prevent unauthorized or unnecessary changes to Salesforce. Option C is incorrect because creating reports to identify which users are leaving blank, and using external data sources to augment the missing data will not address the root cause of the issue, which is the lack of governance in making changes to Salesforce. Option D is incorrect because creating validation rules with error messages to explain why the fields are used will not stop users from seeing new fields on their page that they are unsure of.

 Option C is the best answer because reviewing projected data storage needs is an important step in designing a field-level audit and recovery plan1. You need to estimate how much data storage you will need in the future and plan accordingly. Option A is not correct because reducing data storage by purging old data may not be sufficient or desirable for backup purposes. Option B is not correct because implementing an AppExchange package may not be customized or compatible with your org’s requirements. Option D is not correct because scheduling a weekly export file may not be frequent or granular enough for field-level audit and recovery.

The three characteristics of a Skinny table that help improve report and query performance are: Skinny tables can contain frequently used fields and thereby help avoid joins. Skinny tables are kept in sync with changes to data in the source tables. Skinny tables do not include records that are available in the recycle bin. These characteristics are beneficial because they reduce the query complexity and execution time, and improve the data accuracy and freshness. For example, skinny tables can contain frequently used fields from multiple objects, such as Account and Contact, and thereby help avoid joins that can slow down queries4. Skinny tables are updated automatically when the source tables are modified, so they always reflect the latest data5. Skinny tables do not include records that are available in the recycle bin, so they only contain active records that are relevant for reports and queries.

Implementing duplicate rules (option D) is the best solution to remediate the duplication issue, as it allows the data architect to identify and merge duplicate accounts in Salesforce using native features and tools. Developing an ETL process that utilizes the merge API to merge the duplicate records (option A) is not a good solution, as it may require more coding and testing effort, and it does not prevent duplicates from being created in Salesforce. Utilizing a data warehouse as the system of truth (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not address the duplication issue in Salesforce. Extracting the data using data loader and using excel to merge the duplicate records (option C) is also not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicates from being created in Salesforce.

Using Salesforce duplicate management and using validation rules on new record create and edit are two things that an architect should consider using to maintain data quality for managing Contacts. Salesforce duplicate management allows the architect to create matching rules and duplicate rules to identify, prevent, or allow duplicate records based on various criteria. Validation rules allow the architect to enforce data quality standards and business logic by displaying error messages when users try to save invalid data. The other options are not relevant or helpful for maintaining data quality

According to the What is Master Data Management (MDM)? article, some of the data challenges that an MDM tool can solve are data duplication, data accuracy and quality, and data standardization. The article states that “MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference.” Therefore, an MDM tool can help NTO eliminate data duplication across different systems, improve data accuracy and quality by removing errors and inconsistencies, and standardize data formats and definitions for better integration and analysis.

According to Trailhead2, one of the best practices to increase Salesforce adoption is to migrate existing data from legacy systems or spreadsheets into Salesforce, so that users can access all their data in one place and leverage the features and functionality of Salesforce. Option B is the correct answer because it suggests defining a standard mapping and training sales users to import opportunity data from Excel files into Salesforce, which can help them transition from their old process and increase their confidence and satisfaction with Salesforce. Option A is incorrect because using the Excel connector to Salesforce does not migrate the data into Salesforce, but only syncs it between Excel and Salesforce, which can cause data inconsistency and duplication issues. Option C is incorrect because loading data in an external database and providing access to it does not increase Salesforce adoption, but rather creates another system for users to manage and switch between. Option D is incorrect because creating a chatter group and uploading all Excel files to it does not migrate the data into Salesforce, but only stores it as attachments, which cannot be used for reporting or analysis purposes.

The two features that a data architect should recommend to share records between Salesforce orgs are:

Change Data Capture (CDC): This is a feature that publishes change events for changes in Salesforce records, such as create, update, delete, and undelete operations. Change events can be subscribed by external systems or other Salesforce orgs using various tools, such as Platform Events, Streaming API, or CometD. CDC can help keep data in sync between Salesforce orgs in near real time89.

Salesforce to Salesforce (S2S): This is a feature that allows sharing records and related data with other Salesforce orgs that you partner with. You can choose which records and fields to share, and which orgs to share with. You can also accept updates from the other orgs to keep the data consistent10.

Salesforce Connect is not a good feature to share records between Salesforce orgs because it is intended for integrating external data sources with Salesforce using external objects. Federation Search is not a good feature to share records between Salesforce orgs because it is intended for searching across multiple Salesforce orgs without sharing data.

 Async SOQL with a custom object (option B) and Einstein Analytics (option D) are the two native tools that can be used to report on big object data. Async SOQL allows querying big object data and storing the results in a custom object, which can then be used for reporting. Einstein Analytics can connect to big object data sources and provide advanced analytics and visualization features. Standard reports and dashboards (option A) and standard SOQL queries (option C) cannot be used to report on big object data, as they do not support big object fields

Cleaning data before importing to Salesforce and using Salesforce matching and duplicate rules are two design considerations that an architect should take to minimize data duplication when migrating legacy account data from two aging systems into Salesforce. Cleaning data before importing involves removing or correcting any inaccurate, incomplete, or inconsistent data from the source systems, as well as identifying and resolving any potential duplicates. This ensures that only high-quality and unique data is imported to Salesforce. Using Salesforce matching and duplicate rules allows the architect to define how Salesforce identifies duplicate records during import and how users can handle them. This prevents or reduces the creation of duplicate records in Salesforce and improves data quality. The other options are not effective or recommended for minimizing data duplication.

Building a customer view in Service Console with components that show website data and marketing data as mashup is the best option to fulfill the requirement with minimum development effort in Salesforce. A mashup is a technique that combines data from different sources into a single user interface. You can use Visualforce pages or Lightning components to create mashups that display data from external systems such as your website and marketing system. This way, you can provide your contact center agents with a comprehensive view of the customer information they need.

Pre-go-live automated page-load testing against the Salesforce Full sandbox can help identify and resolve any performance bottlenecks or issues before deploying the solution to production. The Full sandbox is an ideal environment for performance testing as it replicates the production org in terms of data, metadata, and integrations. Stress testing against the web services hosted by the integration middleware can also help evaluate the scalability and reliability of the integration solution under high load conditions.

 Acquiring an AppExchange Lead de-duplication application and implementing Salesforce Matching and Duplicate Rules are two approaches that the manager at Cloud Kicks should take to avoid creating duplicate records when importing Leads into Salesforce. An AppExchange Lead de-duplication application can provide additional features and functionality for finding and preventing duplicate Leads during import, such as fuzzy matching, custom rules, mass merge, etc. Salesforce Matching and Duplicate Rules can allow the manager to define how Salesforce identifies duplicate Leads based on various criteria and how users can handle them during import, such as blocking, allowing, or alerting them. The other options are not feasible or effective for avoiding duplicate records, as they would either not work during import, not provide de-duplication capabilities, or require additional customization.

 Clearing the role field in the generic user record and creating a role at the top of the role hierarchy and assigning it to the generic user are two measures that can help avoid performance issues while working with large number of order records. These measures can prevent data skew and lock contention that may occur when a single user owns or shares a large number of records

According to the official Salesforce guide1, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration. Option A is incorrect because using Bulk API parallel mode for data migration can cause locking issues by processing batches in multiple threads. This mode does not guarantee the order of batch processing and can result in concurrent updates to the same records, which can lead to lock contention and deadlocks. Option C is incorrect because inserting the order in another custom object and using Batch Apex to move the records to Service_Order__c object adds unnecessary complexity and overhead to the data migration process. Option D is incorrect because changing the lookup configuration to “Clear the value of this field” when lookup record is deleted does not address the locking issues caused by data migration, but rather by record deletion.

The two best practices that should be followed when using SOSL for searching are: Keep searches specific and avoid wildcards where possible. Use Find in “ALL FIELDS” for faster searches. These best practices are helpful because they optimize the search performance and relevance. For example, keeping searches specific and avoiding wildcards where possible can reduce the number of results returned and improve the search accuracy. Using Find in “ALL FIELDS” for faster searches can leverage Salesforce’s pre-defined indexes on common fields and return results more quickly than specifying individual fields.

The best solution to resolve the error of “Unable to lock a row” during the account load is to perform batch job in serial mode and reduce batch size. This is because roll-up summary fields are calculated synchronously when the parent record is updated, and asynchronously when the child record is updated. Therefore, updating many child records at once can cause locking issues on the parent record. To avoid this, it is recommended to use serial mode and smaller batch sizes when loading data using tools like Data Loader or Bulk API12. Leverage data loader platform API to load data is not a good option because it does not specify the mode or batch size. Perform batch job in parallel mode and reduce batch size is not a good option because parallel mode can still cause locking issues even with smaller batches. Defer roll-up summary fields calculation during data migration is not a good option because it is not possible to defer or disable roll-up summary fields calculation

The correct answer is B, add custom indexes on frequently searched account and contact object fields. Custom indexes are a way to improve the performance of your queries and reports by creating indexes on specific fields that are often used in filters or joins. By adding custom indexes on frequently searched account and contact object fields, you can speed up the response time for reports and list views. Loading only the data that users are permitted to access, limiting data loading to the 2000 most recent records, or creating a skinny table are also possible solutions, but they are either not feasible, not scalable, or not supported by Salesforce.

Evaluating a third-party AppExchange app, such as OwnBackup or Spanning, etc., for backup and archival purposes is the best approach to address the requirement of having a one-day SLA for any data loss. These apps can provide automated backups, granular restores, and compliance features that can help UC recover from any data loss or corruption scenarios.

Creating a custom object to store a copy of the record when changed and creating a custom object to store the previous and new field values are two possible actions that an architect can perform to meet the requirement of tracking field history on 100 Opportunity fields. A custom object can store more fields and records than the standard field history tracking feature, which has a limit of 20 fields per object and 18 or 24 months of data retention. A custom object can also be used for reporting and analysis of field history data. The other options are not feasible or effective for meeting the requirement