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

In Splunk IT Service Intelligence (ITSI), entities represent infrastructure components, applications, or other elements that are monitored. Each entity is uniquely identified by its entity ID, and entities can be associated with one or more services through the concept of aliases. A problem arises when two or more entities have the same value in a single alias field because aliases are used to match events to entities in ITSI. If multiple entities share the same alias value, ITSI might incorrectly associate data with the wrong entity, leading to inaccurate monitoring and analytics. This scenario requires correction to ensure that each alias uniquely identifies a single entity, thereby maintaining the integrity of the monitoring and analysis process within ITSI. The uniqueness of service IDs, entity IDs, and entity key values in info fields is also important but does not typically present the same level of issue as duplicate values in an alias field.

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.

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.

In a distributed Splunk Enterprise deployment running Splunk IT Service Intelligence (ITSI), theSA‑IndexCreationapp is responsible for creating the necessary custom indexes (such as itsi_summary, itsi_notable, etc.) that ITSI uses to store metrics and notable events. These indexes must exist on the indexer layer becauseindexers are the only Splunk instance type that can actually host and write indexed data. Therefore, SA‑IndexCreation is installed onall indexersin the deployment to ensure that the index definitions are present wherever indexed data is stored. Meanwhile, the main ITSI app (which contains the UI, KPI scheduling, service modeling, analytics, and anomaly detection) is installed onsearch headssince search heads orchestrate searches across the distributed environment and provide ITSI’s interactive features. Universal forwarders and heavy forwarders arenotappropriate targets for SA‑IndexCreation because forwarders do not host writable index locations for ITSI summary and notable event indexes. Thus, the correct installation pattern for SA‑IndexCreation in a distributed environment is on both theindexers and search heads, enabling proper index definition and search functionality across the deployment.

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.

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

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.

B is the correct answer because adaptive thresholding is a feature of ITSI that allows you to dynamically adjust KPI thresholds based on historical patterns and trends. Adaptive thresholding requires a time buffer of at least 15 minutes to calculate the thresholds based on the previous data points. The time buffer ensures that there is enough data to perform the calculations and avoid false positives or negatives. References: Configure adaptive thresholding for a KPI in ITSI

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

When planning and designing a service tree in Splunk ITSI, the focus is onunderstanding how services, components, and dependencies relate to each otherso that ITSI can model service health, impact, and business relevance accurately. A technical diagram of the application and its interconnections provides direct insight into thecomponents and dependency relationshipsthat must be included in the service tree. A report of historical incidents and associated root causes offers valuable context for definingwhich components are critical, where failures have occurred, and how issues propagate, making it highly relevant to service modeling. A service topology from an IT Service Management (ITSM) tool also directly informs the modeling process by showingconfigured relationships between infrastructure and services, which can be imported or referenced to build accurate service trees. In contrast, anorganizational chart of the companyshows the reporting relationships and team structure within the organization — useful for human resource planning or escalation paths, but not directly useful for service tree design in ITSI. Organizational charts do not provide information about application components, their runtime connections, or how failures affect service delivery, making them theleast usefulmaterial when designing a service tree for technical and service health purposes. Therefore, company org charts are generally not relevant to ITSI service 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.

ITSI Saved Search Scheduling is a feature that allows you to schedule searches that run periodically to populate the data for your KPIs. You can configure various settings for your scheduled searches, such as the search frequency, the time range, the cron expression, and so on. One of the settings is realtime_schedule, which controls the way the scheduler computes the next execution time of a scheduled search. The statement that is accurate about this configuration is:

B. If this value is set to 0, the scheduler bases its determination of the next scheduled search on the last search execution time. This is called continuous scheduling. If set to 0, the scheduler never skips scheduled execution periods. However, the execution of the saved search might fall behind depending on the scheduler’s load. Use continuous scheduling whenever you enable the summary index option.

The other statements are not accurate because:

A. If this value is set to 0, the scheduler bases its determination of the next scheduled search execution time on the current time. This is not true because this is what happens when the value is set to 1, not 0.

C. If this value is set to 0, the scheduler may skip scheduled execution periods. This is not true because this is what happens when the value is set to 1, not 0.

D. If this value is set to 0, the scheduler might skip some execution periods to make sure that the scheduler is executing the searches running over the most recent time range. This is not true because this is what happens when the value is set to 1, not 0.

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.

In Splunk IT Service Intelligence (ITSI), creating teams for each department and assigning services to those teams is an effective way to segregate data and ensure that information remains private between different groups within an organization. Teams in ITSI provide a mechanism for role-based access control, allowing administrators to define which users or groups have access to specific services, KPIs, and dashboards. By setting up teams corresponding to each department and then assigning services to these teams, ITSI can accommodate multi-departmental use within the same instance while maintaining strict access controls. This ensures that each department can only view and interact with the data and services relevant to their operations, preserving confidentiality and data integrity across the organization.

In the Notable Events Review dashboard within Splunk IT Service Intelligence (ITSI), when working with a notable event group, users can set or adjust certain attributes at the individual event level or at the group level. These attributes include:

Severity:The importance or impact level of the notable event or group, which can be adjusted to reflect the current assessment of the situation.

Status:The current state of the notable event or group, such as "New," "In Progress," or "Resolved," indicating the progress in addressing the event or group.

Owner:The user or team responsible for managing and resolving the notable event or group.

These settings allow for effective management and tracking of notable events, ensuring that they are appropriately prioritized, acted upon, and resolved by the responsible parties.

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.

After you configure KPI thresholds, you can set up alerts to notify you when aggregate KPI severities change. ITSI generates notable events in Episode Review based on the alerting rules you configure.

Anomaly detection generates notable events when a KPI IT Service Intelligence (ITSI) deviates from an expected pattern.

Notable events are typically generated by a correlation search.

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

ITSI provides a kvstore_to_json.py script that lets you backup/restore ITSI configuration data, perform bulk service KPI operations, apply time zone offsets for ITSI objects, and regenerate KPI search schedules.

When you run a backup job, ITSI saves your data to a set of JSON files compressed into a single ZIP file.

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.

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.