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

Search results are processed, created, and written to the itsi_summary index via an alert action. 

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.

To install Splunk IT Service Intelligence (ITSI) on a single Search Head, one of the straightforward methods is to use the Splunk Web interface, specifically the "Manage Apps" dashboard, to download and install ITSI. This method is user-friendly and does not require manual file handling or command-line operations. By navigating to "Manage Apps" in the Splunk Web interface, users can find ITSI in the app repository or upload the ITSI installation package if it has been downloaded previously. From there, the installation process is initiated through the Splunk Web interface, simplifying the setup process. This approach ensures that the installation follows Splunk's standard app installation procedures, helping to avoid common installation errors and ensuring that ITSI is correctly integrated into the Splunk environment.

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.

In the context of Smart Mode configuration within Splunk IT Service Intelligence (ITSI), the two settings that control how fields affect grouping are "Text similarity" and "Category similarity." Smart Mode is a feature used in event grouping that leverages machine learning to automatically group related events. "Text similarity" refers to how closely the textual content of event fields must match for those events to be grouped together, taking into account commonalities in strings or narratives within the event data. "Category similarity," on the other hand, relates to the similarity in the categorical attributes of events, such as event types or source types, which helps in clustering events that are similar in nature or origin. Both of these settings are crucial in determining how events are grouped in ITSI, influencing the granularity and relevance of the event groupings based on textual and categorical similarities.

Notable events in Splunk IT Service Intelligence (ITSI) are primarily generated through scheduled correlation searches. These searches are designed to monitor data for specific conditions or patterns defined by the ITSI administrator, and when these conditions are met, a notable event is created. These correlation searches are often linked to specific services or groups of services, allowing for targeted monitoring and alerting based on the operational needs of those services. This mechanism enables ITSI to provide timely and relevant alerts that can be further investigated and managed through the Episode Review dashboard, facilitating efficient incident response and management within the IT environment.

Service templates in Splunk IT Service Intelligence (ITSI) are designed to streamline the creation of services by providing pre-defined configurations:

B.Service templates contain KPIs and KPI thresholds:This allows for the standardized deployment of services with predefined performance indicators and their associated thresholds, ensuring consistency across similar services.

C.Service templates can contain specific or generic entity rules:These rules define how entities are associated with services created from the template, allowing for both broad and targeted applicability.

While service templates contain configurations for KPIs, thresholds, and entity rules, the ability to modify templates after services have been instantiated from them is limited. Changes to a template do not retroactively affect services already created from that template. Moreover, service templates do not inherently contain domain-specific dashboards or deep dives; these are created separately within ITSI.

A Multi-KPI alert in Splunk IT Service Intelligence (ITSI) is designed to trigger based on the conditions of multiple Key Performance Indicators (KPIs). This type of alert is particularly useful when a single KPI's state is not sufficient to indicate an issue, but the correlation between multiple KPIs can provide a clearer picture of an emerging problem. The best use case for a Multi-KPI alert is therefore when comparing the values of two or more KPIs indicates an unusual condition is occurring. This allows for more nuanced and context-rich alerting mechanisms that can identify complex issues not detectable by monitoring individual KPIs. This approach is beneficial in complex environments where the interplay between different performance metrics needs to be considered to accurately detect and diagnose issues.

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.

1. From the ITSI main menu, click Configuration > Notable Event Aggregation Policies.

2. Select a custom policy or the Default Policy.

3. Under Smart Mode grouping, enable Smart Mode.

4. Click Select fields. A dialog displays the fields found in your notable events from the last 24 hours.

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.

When an anomaly is detected in Splunk IT Service Intelligence (ITSI), it typically generates a notable event that can be reviewed and managed in the Episode Review dashboard. The Episode Review is part of ITSI's Event Analytics framework and serves as a centralized location for reviewing, annotating, and managing notable events, including those generated by anomaly detection. This process enables IT operators and analysts to efficiently identify, prioritize, and respond to potential issues highlighted by the anomaly alerts. The integration of anomaly alerts into the Episode Review dashboard streamlines the workflow for managing and investigating these alerts within the broader context of IT service management and operational intelligence.

In the context of Splunk IT Service Intelligence (ITSI), a Business Service often has Key Performance Indicators (KPIs) but might not have directly associated entities. Business Services represent high-level aggregations of organizational functions or processes and are typically measured by KPIs that reflect the performance of underlying technical services or components rather than direct infrastructure entities. For example, a Business Service might monitor overall transaction completion times or customer satisfaction scores, which are abstracted from the specific technical entities that underlie these metrics. This abstraction allows Business Services to provide a business-centric view of IT health and performance, focusing on outcomes rather than specific technical components.

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

When creating a custom deep dive, services or KPIs that are in maintenance mode are shown in gray color in the topology view. This indicates that they are not actively monitored and do not generate alerts or notable events. References: Deep Dives

In Splunk IT Service Intelligence (ITSI), glass tables are fully customizable dashboards that provide a visual representation of an organization's IT environment, along with the health and status of services and KPIs. Unlike some pre-configured views or dashboards that might come with default setups in various platforms, ITSI does not provide default glass tables out of the box. Instead, users are encouraged to create their own glass tables tailored to their specific monitoring needs and operational views. This approach ensures that each organization can design glass tables that best represent their unique infrastructure, applications, and service landscapes, providing a more personalized and relevant operational overview.

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.

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.

In the context of Splunk IT Service Intelligence (ITSI), service dependencies allow for the modeling of relationships between services, where the health of one service (dependent) can affect the health of another (primary).

B.It is best practice to use the dependent service's built-in 'ServiceHealthScore' KPI to reflect impact to the primary service:Utilizing the 'ServiceHealthScore' KPI of a dependent service as part of the primary service's health calculation is a recommended practice. This approach ensures that changes in the health of the dependent service directly influence the primary service's overall health score, providing a more holistic view of service health within the IT environment.

C.Setting the dependent service KPI importance level will be treated as any other KPI in the primary service's health score:When a dependent service's KPI is incorporated into a primary service, the importance level assigned to this KPI is factored into the primary service's overall health score calculation just like any other KPI. This means that the impact of the dependent service on the primary service can be weighted according to the business significance of the relationship between the services.

The other options are not accurate representations of ITSI service dependencies. Changes in KPI importance levels do not break dependencies, and there is no restriction on configuring impactful dependent services to only one primary service, as dependencies can be complex and multi-layered across various services.

Provide a value to filter the service to a specific set of entities. These entity rule values are meant to be custom for each service.

To modify default deep dives in Splunk IT Service Intelligence (ITSI), the least permissive role typically required is theitoa_adminrole. This role is specifically designed within ITSI to provide administrative capabilities, including the ability to configure and customize various aspects of ITSI, such as services, KPIs, and deep dives. Theitoa_adminrole has the necessary permissions to edit and manage default deep dives, enabling users with this role to tailor the deep dives to meet specific operational requirements and preferences. Other roles likeitoa_analyst,admin, orpowermight not have sufficient privileges to modify default deep dives, as these roles are generally more restricted in terms of their ability to make broad changes within ITSI.

In Splunk IT Service Intelligence (ITSI), administrators can manually control the grouping of notable events using aggregation policies. Aggregation policies allow for the definition of criteria based on which notable events are grouped together. This includes configuring rules based on event fields, severity, source, or other event attributes. Through these policies, administrators can tailor the event grouping logic to meet the specific needs of their environment, ensuring that related events are grouped in a manner that facilitates efficient analysis and response. This feature is crucial for managing the volume of events and focusing on the most critical issues by effectively organizing related events into manageable groups.