Jan-2024 Pass Splunk SPLK-3002 Exam in First Attempt Easily [Q11-Q31]

Reference:
The service analyzer is a dashboard that allows you to monitor the overall service and KPI status in ITSI. The service analyzer displays a list of all services and their health scores, which indicate how well each service is performing based on its KPIs. You can also view the status and values of each KPI within a service, as well as drill down into deep dives or glass tables for further analysis. The service analyzer helps you identify issues affecting your services and prioritize them based on their impact and urgency. The main purpose of the service analyzer is:
D) Monitor overall service and KPI status. This is true because the service analyzer provides a comprehensive view of the health and performance of your services and KPIs in real time.
The other options are not the main purpose of the service analyzer because:
A) Display a list of all services and entities. This is not true because the service analyzer does not display entities, which are IT components that require management to deliver an IT service. Entities are displayed in other dashboards, such as entity management or entity health overview.
B) Trigger external alerts based on threshold violations. This is not true because the service analyzer does not trigger alerts, which are notifications sent to external systems or users when certain conditions are met. Alerts are triggered by correlation searches or alert actions configured in ITSI.
C) Allow analysts to add comments to alerts. This is not true because the service analyzer does not allow analysts to add comments to alerts, which are notifications sent to external systems or users

Explanation
If set to 0, the scheduler determines the next scheduled search run time based on the last run time for the search. This is called continuous scheduling.

Explanation
The IT Service Intelligence (ITSI) metrics summary index, itsi_summary_metrics, is a metrics-based summary index that stores KPI data.

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. 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.
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A is the correct answer because KPI searches still run during maintenance mode, but the results are buffered until the maintenance window is over. This means that no alerts are triggered during maintenance mode, but once it ends, the buffered results are processed and alerts are generated if necessary. You cannot create new KPIs or modify existing KPIs during maintenance mode. Reference: [Overview of maintenance windows in ITSI]

B is the correct answer because adaptive thresholding is a feature of ITSI that allows you to dynamically adjust KPI thresholds based on historical patterns and trends. Adaptive thresholding requires a time buffer of at least 15 minutes to calculate the thresholds based on the previous data points. The time buffer ensures that there is enough data to perform the calculations and avoid false positives or negatives. Reference: Configure adaptive thresholding for a KPI in ITSI

Reference:
A is the correct answer because when in maintenance mode, KPIs and notable events will begin to be generated again once the window is over. Maintenance mode is a feature of ITSI that allows you to temporarily suspend alerts and health score calculations for a service or an entity during planned maintenance or downtime. During maintenance mode, KPI searches still run, but the results are buffered until the window is over. Once the window is over, the buffered results are processed and alerts and health scores are generated if necessary. Reference: [Overview of maintenance windows in ITSI]

Explanation
Teams provide presentation-layer security only and not data-level security. It’s still possible for a user with access to the Splunk search bar to look up ITSI summary index data.

Explanation
Enable anomaly detection to identify trends and outliers in KPI search results that might indicate an issue with your system.

Explanation
ITSI provides a kvstore_to_json.py script that lets you backup/restore ITSI configuration data, perform bulk service KPI operations, apply time zone offsets for ITSI objects, and regenerate KPI search schedules.
When you run a backup job, ITSI saves your data to a set of JSON files compressed into a single ZIP file.

Reference:
C is the correct answer because a default deep dive initially shows all of the KPIs for a selected service. You can create a default deep dive by drilling down from another dashboard or by selecting a service from the deep dive lister page. A default deep dive does not show health scores, importance scores, or entity swim lanes by default. Reference: [Create default deep dives for services in ITSI]

Reference:
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.

Reference:
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.

Explanation
By default, impacting service health scores have an importance value of 11.

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

C) New KPIs. This is true because when you add new KPIs to a service template, they will be automatically added to all the services that are linked to that template. This helps you keep your services consistent and up-to-date with the latest KPI definitions.
The other options will not be added to linked services by default because:
A) Thresholds. This is not true because when you change thresholds in a service template, they will not affect the existing thresholds in the linked services. You need to manually apply the threshold changes to each linked service if you want them to inherit the new thresholds from the template.
B) Entity rules. This is not true because when you change entity rules in a service template, they will not affect the existing entity rules in the linked services. You need to manually apply the entity rule changes to each linked service if you want them to inherit the new entity rules from the template.
D) Health score. This is not true because when you change health score settings in a service template, they will not affect the existing health score settings in the linked services. You need to manually apply the health score changes to each linked service if you want them to inherit the new health score settings from the template.