Splunk SPLK-3002 Splunk IT Service Intelligence Certified Admin Exam Exam Practice Test

You might need to increase the hardware specifications of your own Enterprise Security deployment above the minimum hardware requirements depending on your environment.

Install Splunk Enterprise Security on a dedicated search head or search head cluster.

The Splunk platform uses indexers to scale horizontally. The number of indexers required in an Enterprise Security deployment varies based on the data volume, data type, retention requirements, search type, and search concurrency.

 D is the correct answer because ITSI provides multiple ways to delete multiple duplicate entities. You can use a CSV upload to overwrite existing entities with new or updated information, or delete them by setting the action field to delete. You can also use the entity lister page to select multiple entities and delete them in bulk. Alternatively, you can use a search command called | deleteentity to delete entities that match certain criteria. References: Create and update entities using a CSV file in ITSI, Delete entities in bulk in ITSI, Delete entities using the | deleteentity command in ITSI

SA-IndexCreation is required on all indexers. For non-clustered, distributed environments, copy SA-IndexCreation to $SPLUNK_HOME/etc/apps/ on individual indexers.

For Entity Cohesion anomaly detection in Splunk IT Service Intelligence (ITSI), the minimum number of entities a KPI must be split by is 2. Entity Cohesion as a method of anomaly detection focuses on identifying anomalies based on the deviation of an entity's behavior in comparison to other entities within the same group or cohort. By requiring a minimum of only two entities, ITSI allows for the comparison of entities to detect significant deviations in one entity's performance or behavior, which could indicate potential issues. This method leverages the idea that entities performing similar functions or within the same service should exhibit similar patterns of behavior, and significant deviations could be indicative of anomalies. The low minimum requirement of two entities ensures that this powerful anomaly detection feature can be utilized even in smaller environments.

Creating a custom module in Splunk IT Service Intelligence (ITSI) is particularly beneficial for the purpose of migrating KPI base searches and related visualizations to other ITSI installations. Custom modules can encapsulate a set of configurations, searches, and visualizations that are tailored to specific monitoring needs or environments. By packaging these elements into a module, it becomes easier to transfer, deploy, and maintain consistency across different ITSI instances. This modularity supports the reuse of developed components, simplifying the process of scaling and replicating monitoring setups in diverse operational contexts. The ability to migrate these components seamlessly enhances operational efficiency and ensures that best practices and custom configurations can be shared across an organization's ITSI deployments.

In Splunk IT Service Intelligence (ITSI), default deep dives are auto-generated and can be accessed via the Service Analyzer. Deep dives are an essential feature of ITSI that provide an in-depth, granular view into the health and performance of services and their associated KPIs. These default deep dives are automatically created for each service, allowing users to quickly drill down into the detailed operational metrics and performance data of their services. By accessing these deep dives through the Service Analyzer, ITSI users can efficiently investigate issues, understand service dependencies, and make informed decisions to maintain optimal service health. The auto-generated nature of these default deep dives simplifies the monitoring and analysis process, providing immediate insights into service performance without the need for manual setup or configuration.

Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).

One of the recommended best practices for Splunk IT Service Intelligence (ITSI) installation is to avoid installing ITSI on search heads that already have Splunk Enterprise Security (ES) installed. This recommendation stems from potential resource conflicts and performance issues that can arise when both resource-intensive applications are deployed on the same instance. Both ITSI and ES are complex applications that require significant system resources to function effectively, and running them concurrently on the same search head can lead to degraded performance, conflicts in resource allocation, and potential stability issues. It's generally advised to segregate these applications onto separate Splunk instances to ensure optimal performance and stability for both platforms.

C. New KPIs. This is true because when you add new KPIs to a service template, they will be automatically added to all the services that are linked to that template. This helps you keep your services consistent and up-to-date with the latest KPI definitions.

The other options will not be added to linked services by default because:

A. Thresholds. This is not true because when you change thresholds in a service template, they will not affect the existing thresholds in the linked services. You need to manually apply the threshold changes to each linked service if you want them to inherit the new thresholds from the template.

B. Entity rules. This is not true because when you change entity rules in a service template, they will not affect the existing entity rules in the linked services. You need to manually apply the entity rule changes to each linked service if you want them to inherit the new entity rules from the template.

D. Health score. This is not true because when you change health score settings in a service template, they will not affect the existing health score settings in the linked services. You need to manually apply the health score changes to each linked service if you want them to inherit the new health score settings from the template.

To automatically create ServiceNow incidents when a Multi-KPI alert triggers in Splunk IT Service Intelligence (ITSI), the following approaches can be used:

C.By creating a notable event aggregation policy with a ServiceNow (SNOW) incident action:ITSI allows the creation of notable event aggregation policies that can specify actions to be taken when certain conditions are met. One of these actions can be the creation of an incident in ServiceNow, directly linking the alerting mechanism in ITSI with incident management in ServiceNow.

D.By editing the associated correlation search and specifying an alert action:Correlation searches in ITSI are used to identify patterns or conditions that signify notable events. These searches can be configured to include alert actions, such as creating a ServiceNow incident, whenever the search conditions are met. This direct integration ensures that incidents are automatically generated in ServiceNow, based on the specific criteria defined in the correlation search.

Options A and B are not standard practices for integrating ITSI with ServiceNow for automatic incident creation. The configuration typically involves setting up actionable alert mechanisms within ITSI that are specifically designed to integrate with external systems like ServiceNow.

When an episode warrants investigation, the analyst acknowledges the episode, which moves the status from New to In Progress.

To utilize Adaptive Thresholding in Splunk IT Service Intelligence (ITSI), the minimum requirement for a set of Key Performance Indicator (KPI) data is that it must be at least 7 days old. Adaptive Thresholding uses historical data to dynamically adjust thresholds based on observed patterns and trends. Having a minimum of 7 days worth of data allows the system to analyze a sufficient amount of information to identify normal ranges and variances in KPI behavior, thereby setting more accurate and contextually relevant thresholds. This requirement ensures that the adaptive thresholds are based on a meaningful data set that reflects the typical operational conditions of the monitored services.

A KPI lane is a type of deep dive swim lane that does not require writing SPL. You can simply select a service and a KPI from a drop-down list and ITSI will automatically populate the lane with the corresponding data. You can also adjust the threshold settings and time range for the KPI lane. References: [KPI Lanes]

In Splunk IT Service Intelligence (ITSI), when using multiple event management policies, it is important to understand that policy processing is applied in a defined order. This order is crucial because it determines how events are processed and aggregated, and which rules are applied to events first. The order of policies can be customized, allowing administrators to prioritize certain policies over others based on the specific needs and operational logic of their IT environment. This feature provides flexibility in event management, enabling more precise control over event processing and ensuring that the most critical events are handled according to the desired precedence. This structured approach to policy processing helps in maintaining the efficiency and effectiveness of event management within ITSI.

In Splunk IT Service Intelligence (ITSI), when a Key Performance Indicator (KPI) aggregate value is calculated, thetstatsfunction is often called. Thetstatsfunction in Splunk is used for rapid statistical queries over large volumes of data, which is particularly useful in ITSI for efficiently calculating aggregate values of KPIs across potentially vast datasets. This function allows for quick aggregation and summarization of indexed data, which is essential for monitoring and analyzing the performance metrics that KPIs represent in ITSI. Unlike thestatscommand, which operates on already retrieved events,tstatsworks directly on indexed data, providing faster performance especially when dealing with high volumes of data typical in an IT environment. Thetstatscommand is therefore fundamental in the backend processing of ITSI for calculating aggregate values of KPIs, enabling real-time and historical analysis of service health and performance.

Create a glass table to visualize and monitor the interrelationships and dependencies across your IT and business services.

The service swapping settings are saved and apply the next time you open the glass table.

You can add metrics like KPIs, ad hoc searches, and service health scores that update in real time against a background that you design. Glass tables show real-time data generated by KPIs and services.

Time policies are user-defined threshold values to be used at different times of the day or week to account for changing KPI workloads. Time policies accommodate normal variations in usage across your services and improve the accuracy of KPI and service health scores. For example, if your organization’s peak activity is during the standard work week, you might create a KPI threshold time policy that accounts for higher levels of usage during work hours, and lower levels of usage during off-hours and weekends. The statement that applies when configuring time policies for KPI thresholds is:

B. They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00. This is true because time policies allow you to define different threshold values for different time blocks, such as AM/PM, work hours/off hours, weekdays/weekends, and so on. This way, you can account for the expected variations in your KPI data based on the time of day or week.

The other statements do not apply because:

A. A person can only configure 24 policies, one for each hour of the day. This is not true because you can configure more than 24 policies using different time block combinations, such as 3 hour block, 2 hour block, 1 hour block, and so on.

C. If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use it. This is not true because time policies are designed to handle KPIs that change significantly through a cycle on a daily basis, such as web traffic volume or CPU load percent.

D. It is possible for multiple time policies to overlap. This is not true because you can only have one active time policy at any given time. When you create a new time policy, the previous time policy is overwritten and cannot be recovered.

Splunk IT Service Intelligence (ITSI) is designed to align IT monitoring with the business outcomes that matter to stakeholders — especially executives who are focused on service performance and its impact on revenue, customer experience, and operational goals. In ITSI,entitiesrepresent the individual components that make up services and contribute to Key Performance Indicators (KPIs). Selecting the right entities for service modeling is critical: technical entities (like hosts or network interfaces) are useful for IT operations troubleshooting, but they don’t inherently represent business outcomes. Executive leadership cares about how incidents affect business capabilities and outcomes — such as sales performance, customer transactions, and channel health. Therefore, entities that reflectbusiness context(for example, store locations, product categories, or payment types) map IT issues directly to business performance indicators. When executives can see service health and incidents broken down by these business‑centric entities, they gainimpactful visibilityinto how issues affect revenue, customer interactions, and overall business operations. In contrast, purely technical entities such as hosts or network interfaces do not provide that business impact perspective, and demographic slices like season or customer age — while potentially valuable for marketing — don’t directly connect IT service health to business service performance in ITSI modeling.

By default, impacting service health scores have an importance value of 11.

Before a module can be created in Splunk IT Service Intelligence (ITSI), it is essential to have one or more datamodels established. Datamodels in Splunk provide a structured format for organizing and interpreting data, which is crucial for modules within ITSI. Modules often rely on datamodels to extract, transform, and present data in a meaningful way, especially when dealing with complex datasets across various sources. Datamodels serve as the foundation for the module's ability to categorize and analyze data efficiently, enabling the creation of KPIs, services, and visualizations that are aligned with the specific needs of the module. Having these datamodels in place ensures that the module can function correctly and provide valuable insights into the monitored IT environments.