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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.

By default, notable event metadata is archived after six months to keep the KV store from growing too large.

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.Reference:Service Analyzer

When creating a custom deep dive, services or KPIs that are in maintenance mode are shown in gray color in the topology view. This indicates that they are not actively monitored and do not generate alerts or notable events.Reference:Deep Dives

A KPI lane is a type of deep dive swim lane that does not require writing SPL. You can simply select a service and a KPI from a drop-down list and ITSI will automatically populate the lane with the corresponding data. You can also adjust the threshold settings and time range for the KPI lane.Reference: [KPI Lanes]

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.Reference: [Anomaly Detection]

It's a best practice to schedule maintenance windows with a 15- to 30-minute time buffer before and after you start and stop your maintenance work.A maintenance window is a period of time when a service or entity is undergoing maintenance operations or does not require active monitoring. It is a best practice to schedule maintenance windows with a 15- to 30-minute time buffer before and after you start and stop your maintenance work. This gives the system an opportunity to catch up with the maintenance state and reduces the chances of ITSI generating false positives during maintenance operations. For example, if a server will be shut down for maintenance at 1:00PM and restarted at 5:00PM, the ideal maintenance window is 12:30PM to 5:30PM. The 15- to 30-minute time buffer is a rough estimate based on 15 minutes being the time period over which most KPIs are configured to search data and identify alert triggers.Reference:Overview of maintenance windows in ITSI

When an episode warrants investigation, the analyst acknowledges the episode, which moves the status fromNewtoIn Progress.An episode represents a disruption of service operation causing impact to business operations. It is a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation. In Episode Review, you can manage the episodes and their statuses using various actions. One of the actions is Acknowledge, which changes the status of an episode from New to Acknowledged and assigns the current user as the owner. This action indicates that someone is working on resolving the episode and prevents duplicate efforts from other users.Reference:Overview of Episode Review in ITSI, [Episode actions in Episode Review]