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/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, ]