Which of the following is an advantage of using adaptive time thresholds?

Question

Options

• AAutomatically update thresholds daily to manage dynamic changes to KPI values. This is true
• BAutomatically adjust KPI calculation to manage dynamic event data.
• CAutomatically adjust aggregation policy grouping to manage escalating severity.
• DAutomatically adjust correlation search thresholds to adjust sensitivity over time.

• A
  95% (62)
• B
  2% (1)
• D
  3% (2)

Explanation

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: 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.

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