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

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/TimePolicies, Adaptive thresholds are thresholds calculated by machine learning algorithms that dynamically adapt and change based on the KPI’s observed behavior. Adaptive thresholds are useful for monitoring KPIs that have unpredictable or seasonal patterns that are difficult to capture with static thresholds. For example, you might use adaptive thresholds for a KPI that measures web traffic volume, which can vary depending on factors such as holidays, promotions, events, and so on. The advantage of using adaptive thresholds is:, A. Automatically update thresholds daily to manage dynamic changes to KPI values. This is true because adaptive thresholds use historical data from a training window to generate threshold values for each time block in a threshold template. Each night at midnight, ITSI recalculates adaptive threshold values for a KPI by organizing the data from the training window into distinct buckets and then analyzing each bucket separately. This way, the thresholds reflect the most recent changes in the KPI data and account for any anomalies or trends., The other options are not advantages of using adaptive thresholds because:, B. Automatically adjust KPI calculation to manage dynamic event data. This is not true because adaptive thresholds do not affect the KPI calculation, which is based on the base search and the aggregation method. Adaptive thresholds only affect the threshold values that are used to determine the KPI severity level., C. Automatically adjust aggregation policy grouping to manage escalating severity. This is not true because adaptive thresholds do not affect the aggregation policy, which is a set of rules that determines how to group notable events into episodes. Adaptive thresholds only affect the threshold values that are used to generate notable events based on KPI severity level., D. Automatically adjust correlation search thresholds to adjust sensitivity over time. This is not true because adaptive thresholds do not affect the correlation search, which is a search that looks for relationships between data points and generates notable events. Adaptive thresholds only affect the threshold values that are used by KPIs, which can be used as inputs for correlation searches., References: Create adaptive KPI thresholds in ITSI, ]

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/MKA, A best practice for identifying the most effective services with which to start an iterative ITSI deployment is to analyze the business to determine the most critical services that have the most impact on revenue, customer satisfaction, or other key performance indicators. You can use the Service Analyzer to prioritize and monitor these services. References: Service Analyzer, , ]

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/DeepDives, A deep dive is a dashboard that allows you to analyze the historical trends and anomalies of your KPIs and metrics in ITSI. A deep dive displays a timeline of events and swim lanes of data that you can customize and filter to investigate issues and perform root cause analysis. Some of the capabilities of deep dives are:, B. Visualizing one or more service KPIs values by time. This is true because you can add KPI swim lanes to a deep dive to show the values and severity levels of one or more KPIs over time. You can also compare KPIs from different services or entities using service swapping or entity splitting., C. Examining and comparing alert levels for KPIs in a service over time. This is true because you can add alert swim lanes to a deep dive to show the alert levels and counts for one or more KPIs over time. You can also drill down into the alert details and view the notable events associated with each alert., D. Comparing swim lane values for a slice of time. This is true because you can use the time range selector to zoom in or out of a specific time range in a deep dive. You can also use the time brush to select a slice of time and compare the swim lane values for that time period., The other option is not a capability of deep dives because:, A. Comparing a service’s notable events over a time period. This is not true because deep dives do not display notable events, which are alerts generated by ITSI based on certain conditions or correlations. Notable events are displayed in other dashboards, such as episode review or glass tables., References: [Overview of deep dives in ITSI], [Add swim lanes to a deep dive in ITSI], ]