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

In Splunk IT Service Intelligence (ITSI), notable event groups are used to logically group related notable events, which enhances the manageability and analysis of events:

A.Notable event groups combine independent notable events:This characteristic allows for the aggregation of related events into a single group, making it easier for users to manage and investigate related issues. By grouping events, users can focus on the broader context of an issue rather than getting lost in the details of individual events.

While notable event groups play a critical role in organizing and managing events in ITSI, they do not inherently allow users to adjust threshold settings, which is typically handled at the KPI or service level. Additionally, while notable event groups are utilized within the ITSI framework, the statement that they are created in the 'itsi_tracked_alerts' index might not fully capture the complexity of how event groups are managed and stored within the ITSI architecture.

D is the correct answer because teams allow you to restrict access to service content in UI views such as service analyzers, glass tables, deep dives, and episode review. Teams alsocontrol access to services and KPIs for editing and viewing purposes. Teams do not affect the ability to search against the itsi_summary index, restrict notable event alert actions, or restrict searches against the itsi_notable_audit index. References: Overview of teams in ITSI

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.