SPLK-3002 Splunk IT Service Intelligence Certified Admin Exam Questions and Answers

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

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.

In Splunk IT Service Intelligence (ITSI), teams are used to organize services, KPIs, and other objects within ITSI to facilitate access control and management:

B.Services should be assigned to the 'global' team if all users need access to it:The 'global' team in ITSI is a built-in concept that denotes universal accessibility. Assigning services to the 'global' team makes them accessible to all ITSI users, irrespective of their specific team memberships. This is useful for services that are relevant across the entire organization.

C.By default, all services are owned by the built-in 'global' team and administered by the 'itoa_admin' role:This default setting ensures that upon creation, services are accessible to administrators and can be further re-assigned or refined for access by specific teams as needed.

D.A new team admin role should be created for each team. The new role should inherit the 'itoa_team_admin' role:This best practice allows for granular access control and management within teams. Each team can have its own administrators with the appropriate level of access and permissions tailored to the needs of that team, derived from the capabilities of the 'itoa_team_admin' role.

The concept of adding 'itoa admin roles' with read-only permissions contradicts the typical use case for administrative roles, which usually require more than read-only access to manage services and entities effectively.

In Splunk IT Service Intelligence (ITSI), the Service Health Score is a metric that provides a quantifiable measure of the overall health and performance of a service. The score ranges from 0 to 100, with higher scores indicating better health. The range for a normal Service Health score category is typically from 80 to 100. Scores within this range suggest that the service is performing well, with no significant issues affecting its health. This categorization helps IT and business stakeholders quickly assess the operational status of their services, enabling them to focus on services that may require attention or intervention due to lower health scores.

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.

It's a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation.

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.

Splunk ITSI offers two built‑in anomaly detection algorithms:TrendingandEntity Cohesion. TheTrendingalgorithm works on theaggregate KPI series, comparing recent KPI behavior with its historical pattern to detect unusual trending patterns over time. It doesnotevaluate behavior across separate entities within the KPI split — it simply looks at deviations from historical trends in the combined KPI values. On the other hand, theEntity Cohesionalgorithm is specifically designed to detect when entities that are expected to behave similarly begin to diverge in behavior. When a KPI is split by entity (for example, multiple servers, locations, or service tiers), Entity Cohesion normalizes each entity’s time series and compares them against each other. If one entity’s pattern differs significantly from the group’s patterns, it is flagged as an anomaly. This matches the “paired monitoring requirement” of producing an alert whenone entity in the KPI is not behaving similarly to the other entities. The option describing entity cohesion paired with that requirement reflects the correct use case for this algorithm in ITSI. Neither trending anomaly detection nor entity cohesion anomaly detection is intended to detect multiple KPIs deviating at the service level — such cross‑KPI alerts are handled by other alerting constructs like multi‑KPI alerts or correlation searches, not these specific anomaly algorithms.

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.

Copy SA-IndexCreation to $SPLUNK_HOME/etc/apps/ on all individual indexers in your environment.

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.

Custom deep dives in Splunk IT Service Intelligence (ITSI) are versatile and highly customizable dashboards that allow users to analyze various types of data in a unified view. One of the key characteristics of custom deep dives is their ability to combine lanes of different data types, such as metrics, events, Key Performance Indicators (KPIs), and service health scores. This multifaceted approach provides a comprehensive and layered view of the IT environment, enabling analysts and operators to correlate different data types and gain deeper insights into the health and performance of services. By incorporating these diverse data lanes, custom deep dives facilitate a more holistic understanding of the operational landscape, aiding in more effective troubleshooting and decision-making.

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.

During the data audit phase of requirements gathering for Splunk IT Service Intelligence (ITSI), it's crucial to identify and record the meta-data for entities, focusing on information (info) and aliases. This step involves understanding and documenting the key attributes and identifiers that describe each entity, such as host names, IP addresses, device types, or other relevant characteristics. These attributes are used to categorize and uniquely identify entities within ITSI, enabling more effective mapping of data to services and KPIs. By meticulously recording this meta-data, organizations ensure that their ITSI implementation is aligned with their specific monitoring needs and infrastructure, facilitating accurate service modeling and event management. This practice is foundational for setting up ITSI to reflect the actual IT environment, enhancing the relevance and effectiveness of the monitoring and analysis capabilities.

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.

Deep dives in Splunk IT Service Intelligence (ITSI) allow for an in-depth analysis of services and their KPIs over time, providing a detailed view of the operational health and performance trends. One of the powerful actions that can be performed with a deep dive is the creation of a Multi-KPI alert from the deep dive's current state. This functionality enables users to define alerts based on the complex conditions observed during the deep dive analysis, allowing for the early detection of similar situations in the future. By configuring a Multi-KPI alert directly from a deep dive, ITSI users can leverage their insights and observations to proactively monitor for patterns or conditions that may indicate potential service degradation or failure, enhancing the overall responsiveness and effectiveness of the IT monitoring strategy.

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.

 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