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

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/AD, Anomaly detection is a feature of ITSI that uses machine learning to detect when KPI data deviates from a normal pattern. The following items apply to anomaly detection:, B. A minimum of 24 hours of data is needed for anomaly detection, and a minimum of 4 entities for cohesive analysis. This ensures that there is enough data to establish a baseline pattern and compare different entities within a service., C. Anomaly detection automatically generates notable events when KPI data diverges from the pattern. You can configure the sensitivity and severity of the anomaly detection alerts and assign them to episodes or teams. References: [Anomaly Detection], ]

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/EA/TroubleshootRE, The index that will contain useful error messages when troubleshooting ITSI issues is:, B. _internal. This is true because the _internal index contains logs and metrics generated by Splunk processes, such as splunkd and metrics.log. These logs can help you diagnose problems with your Splunk environment, including ITSI components and features., The other indexes will not contain useful error messages because:, A. _introspection. This is not true because the _introspection index contains data about Splunk resource usage, such as CPU, memory, disk space, and so on. These data can help you monitor the performance and health of your Splunk environment, but not the error messages., C. itsi_summary. This is not true because the itsi_summary index contains summarized data for your KPIs and services, such as health scores, severity levels, threshold values, and so on. These data can help you analyze the trends and anomalies of your IT services, but not the error messages., D. itsi_notable_audit. This is not true because the itsi_notable_audit index contains audit data for your notable events and episodes, such as creation time, owner, ]