Free Practice Questions for Splunk SPLK-3002 Exam

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.

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.

 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

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]

Among the anomaly detection algorithms included within Splunk IT Service Intelligence (ITSI), "Entity Cohesion" is a notable option. The Entity Cohesion algorithm is designed to detect anomalies by comparing the behavior of one entity against the collective behavior of a group of similar entities. This approach is particularly useful in scenarios where entities are expected to exhibit similar patterns of behavior under normal conditions. Anomalies are identified when an entity's metrics deviate significantly from the group norm, suggesting a potential issue with that specific entity. This method leverages the concept of cohesion among similar entities to enhance the accuracy and relevance of anomaly detection within ITSI environments.