Download Splunk.SPLK-3002.CertDumps.2024-08-01.21q.tqb

ITSI Saved Search Scheduling is a feature that allows you to schedule searches that run periodically to populate the data for your KPIs. You can configure various settings for your scheduled searches, such as the search frequency, the time range, the cron expression, and so on. One of the settings is realtime_schedule, which controls the way the scheduler computes the next execution time of a scheduled search. The statement that is accurate about this configuration is:B) If this value is set to 0, the scheduler bases its determination of the next scheduled search on the last search execution time. This is called continuous scheduling. If set to 0, the scheduler never skips scheduled execution periods. However, the execution of the saved search might fall behind depending on the scheduler's load. Use continuous scheduling whenever you enable the summary index option.The other statements are not accurate because:A) If this value is set to 0, the scheduler bases its determination of the next scheduled search execution time on the current time. This is not true because this is what happens when the value is set to 1, not 0.C) If this value is set to 0, the scheduler may skip scheduled execution periods. This is not true because this is what happens when the value is set to 1, not 0.D) If this value is set to 0, the scheduler might skip some execution periods to make sure that the scheduler is executing the searches running over the most recent time range. This is not true because this is what happens when the value is set to 1, not 0.

The KPI importance weight is a value that indicates how much a KPI contributes to the overall health score of a service. The importance weight can range from 1 (lowest) to 10 (highest). The statement that applies when configuring a KPI importance weight of 11 is:B) Importance weight is unused for health scoring. This is true because an importance weight of 11 is invalid and cannot be used for health scoring. The maximum value for importance weight is 10.The other statements do not apply because:A) At least 10% of the KPIs will go critical. This is not true because an importance weight of 11 does not affect the severity level of any KPIs.C) The service will go critical. This is not true because an importance weight of 11 does not affect the health score or status of any service.D) It is a minimum health indicator KPI. This is not true because an importance weight of 11 does not indicate anything about the minimum health level of a KPI.

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.