SPLK-3002 Exam Dumps - Splunk IT Service Intelligence Certified Admin 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.

 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

In Splunk IT Service Intelligence (ITSI), the Service Analyzer visually represents service dependencies. Asolidline between services indicates a normal one‑way dependency where one service’s health contributes to another’s service health score. However, adottedline signifies acyclic dependency, meaning that two services are defined as depending on each other in a loop. This typically happens when a service is configured to depend on another service that, directly or indirectly, also depends back on the first service. In Helga’s scenario, because the web service is set to depend on the database using the Heartbeat KPI, and the configuration somehow established a reverse dependency (even inadvertently), the Service Analyzer shows the relationship as cyclic with a dotted line. To resolve this, Helga needs to check the service dependency configuration for the database service and ensure it doesnotmistakenly include the web service (or any chain of dependencies that leads back to it). Removing or correcting that erroneous dependency breaks the cycle, which will then change the representation to a solid line and properly reflect the dependency without circular references. It’s not related to KPI importance values in this context — importance affects health score calculation but does not cause a cyclic dependency indicator.

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.

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.

A is the correct answer because anomaly detection can be enabled on a KPI level in ITSI. Anomaly detection allows you to identify trends and outliers in KPI search results that might indicate an issue with your system. You can enable anomaly detection for a KPI by selecting one of the two anomaly detection algorithms in the KPI configuration panel. References: Apply anomaly detection to a KPI in ITSI