Splunk SPLK-3002 Exam Dumps

Search results are processed, created, and written to the itsi_summary index via an alert action.

D is the correct answer because KPI search results are stored in the itsi_summary index in ITSI. This index is an events index that stores the results of scheduled KPI searches. Summary indexing lets you run fast searches over large data sets by spreading out the cost of a computationally expensive report over time. Reference:Overview of ITSI indexes

A multi-KPI alert is a type of correlation search that is based on defined trigger conditions for two or more KPIs. When trigger conditions occur simultaneously for each KPI, the search generates a notable event. For example, you might create a multi-KPI alert based on two common KPIs: CPU load percent and web requests. A sudden simultaneous spike in both CPU load percent and web request KPIs might indicate a DDOS (Distributed Denial of Service) attack. Multi-KPI alerts can bring such trending behaviors to your attention early, so that you can take action to minimize any impact on performance. Multi-KPI alerts are useful for correlating the status of multiple KPIs across multiple services. They help you identify causal relationships, investigate root cause, and provide insights into behaviors across your infrastructure. The best use case for configuring a multi-KPI alert is to raise an alert when one or more KPIs indicate an outage is occurring, such as when the service health score drops below a certain threshold or when multiple KPIs have critical severity levels. Reference:Create multi-KPI alerts in ITSI

B is the correct answer because value over time is a valid type of Multi-KPI Alert in ITSI. A Multi-KPI Alert is a type of alert that triggers when multiple KPIs from one or more services meet certain conditions within a specified time range. Value over time is a condition that compares the current value of a KPI to its previous values over a specified time range. For example, you can create a Multi-KPI Alert that triggers when the CPU usage and memory usage of a service are both higher than their average values in the last 24 hours. Reference: [Create Multi-KPI alerts in ITSI], [Multi-KPI alert conditions in ITSI]

KPIbase searcheslet you share a search definition across multiple KPIs in IT Service Intelligence (ITSI). Create base searches to consolidate multiple similar KPIs, reduce search load, and improve search performance.

A base search is a search definition that can be shared across multiple KPIs that use the same data source. Base searches can improve search performance and reduce search load by consolidating multiple similar KPIs. The statement that accurately describes base searches used for KPIs in a service is:

A . Base searches can be used for multiple services. This means that you can create a base search for a service and use it for other services that have similar data sources and KPIs. For example, if you have multiple services that monitor web server performance, you can create a base search that queries the web server logs and use it for all the services that need to calculate KPIs based on those logs.

Splunk ITSI offers two built in anomaly detection algorithms: Trending and Entity Cohesion. The Trending algorithm works on the aggregate KPI series, comparing recent KPI behavior with its historical pattern to detect unusual trending patterns over time. It does not evaluate 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, the Entity Cohesion algorithm 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 when one 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.