Reference:
A, B, and C are correct answers because service access control requirements for ITSI Team Access should be considered before creating the ITSI Service, as different teams may have different permissions and views of the service data. Entities, entity meta-data, and entity rules should also be planned carefully to support the service design and configuration, as they determine how ITSI maps data sources to services and KPIs. Services, entities, and saved searches are stored in the ITSI app, while events created by KPI execution are stored in the itsi_summary index for faster retrieval and analysis. Reference: ITSI service design best practices, Overview of ITSI indexes

Provide a value to filter the service to a specific set of entities. These entity rule values are meant to be custom for each service.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/EntityRules A is the correct answer because a service must define entity rules if the intention is for the KPIs in the service to filter to only entities assigned to the service. Entity rules are filters that match entities to services based on entity aliases or entity metadata. If you enable the Filter to Entities in Service option for a KPI, you need to define entity rules for the service to ensure that the KPI search results only include the relevant entities for the service. Otherwise, the KPI search results might include entities that are not part of the service or exclude entities that are part of the service. References: [Define entities for a service in ITSI], [Configure KPI settings in ITSI]

The IT Service Intelligence (ITSI) metrics summary index, itsi_summary_metrics, is a metrics-based summary index that stores KPI data.
Reference:
A is the correct answer because the itsi_summary_metrics index is used to store KPI values in ITSI. This index improves the performance of the searches dispatched by ITSI, particularly for very large environments. Every KPI is summarized in both the itsi_summary events index and the itsi_summary_metrics metrics index. Reference: Overview of ITSI indexes

Time policies are user-defined threshold values to be used at different times of the day or week to account for changing KPI workloads. Time policies accommodate normal variations in usage across your services and improve the accuracy of KPI and service health scores. For example, if your organization’s peak activity is during the standard work week, you might create a KPI threshold time policy that accounts for higher levels of usage during work hours, and lower levels of usage during off-hours and weekends. The statement that applies when configuring time policies for KPI thresholds is:
* B. They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00.
This is true because time policies allow you to define different threshold values for different time blocks, such as AM/PM, work hours/off hours, weekdays/weekends, and so on. This way, you can account for the expected variations in your KPI data based on the time of day or week.
The other statements do not apply because:
* A. A person can only configure 24 policies, one for each hour of the day. This is not true because you can configure more than 24 policies using different time block combinations, such as 3 hour block, 2 hour block, 1 hour block, and so on.
* C. If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use it. This is not true because time policies are designed to handle KPIs that change significantly through a cycle on a daily basis, such as web traffic volume or CPU load percent.
* D. It is possible for multiple time policies to overlap. This is not true because you can only have one active time policy at any given time. When you create a new time policy, the previous time policy is overwritten and cannot be recovered.
References: Create time-based static KPI thresholds in ITSI

By default, impacting service health scores have an importance value of 11.
Reference:
A service template is a predefined set of KPIs and entity rules that you can apply to a service or a group of services. A service template helps you standardize the configuration and monitoring of similar services across your IT environment. A service template can also include dependent services, which are services that are required for another service to function properly. For example, a web server service might depend on a database service and a network service. The default importance value for dependent services’ health scores is:
D) 10. This is true because the importance value indicates how much a dependent service contributes to the health score of the parent service. The default value is 10, which means that the dependent service has the highest impact on the parent service’s health score. You can change the importance value of a dependent service in the service template settings.
The other options are not correct because:
A) 11. This is not true because 11 is an invalid value for importance. The valid range is from 1 (lowest) to 10 (highest).
B) 1. This is not true because 1 is the lowest value for importance, not the default value. A value of 1 means that the dependent service has the lowest impact on the parent service’s health score.
C) Unassigned. This is not true because every dependent service has an assigned importance value, which defaults to 10.

Reference:
B is the correct answer because dynamic field values can be specified with <fieldname /fieldname> syntax within a correlation search. This syntax allows you to insert values from fields returned by the correlation search into alert actions such as email subject or body. For example, <host /host> inserts the value of the host field into the email. Reference: [Use dynamic field values in correlation searches in ITSI]

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.

Notable events in Splunk IT Service Intelligence (ITSI) are primarily generated through scheduled correlation searches. These searches are designed to monitor data for specific conditions or patterns defined by the ITSI administrator, and when these conditions are met, a notable event is created. These correlation searches are often linked to specific services or groups of services, allowing for targeted monitoring and alerting based on the operational needs of those services. This mechanism enables ITSI to provide timely and relevant alerts that can be further investigated and managed through the Episode Review dashboard, facilitating efficient incident response and management within the IT environment.

After you configure KPI thresholds, you can set up alerts to notify you when aggregate KPI severities change.
ITSI generates notable events in Episode Review based on the alerting rules you configure.
Anomaly detection generates notable events when a KPI IT Service Intelligence (ITSI) deviates from an expected pattern.
Notable events are typically generated by a correlation search.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/KPIthresholds
https://docs.splunk.com/Documentation/ITSI/4.10.1/SI/AboutSI
A, B, and D are correct answers because ITSI can generate notable events when a KPI breaches a threshold, when a KPI detects an anomaly, or when a correlation search matches a defined pattern. These are the main ways that ITSI can alert you to potential issues or incidents in your IT environment. References: Configure KPI thresholds in ITSI, Apply anomaly detection to a KPI in ITSI, Generate events with correlation searches in ITSI

In the context of troubleshooting KPI search performance in Splunk IT Service Intelligence (ITSI), the search names in the job activity that identify base searches typically follow the pattern “Indicator – Shared – xxxx – ITSI Search.” These base searches are fundamental components of the KPI calculation process, aggregating and preparing data for further analysis by KPIs. Identifying these base searches in the job activity is crucial for diagnosing performance issues, as these searches can be resource-intensive and impact overall system performance. Understanding the naming convention helps administrators and analysts quickly pinpoint the base searches related to specific KPIs, facilitating more effective troubleshooting and optimization of search performance within the ITSI environment.

Create a glass table to visualize and monitor the interrelationships and dependencies across your IT and business services.
The service swapping settings are saved and apply the next time you open the glass table.
You can add metrics like KPIs, ad hoc searches, and service health scores that update in real time against a background that you design. Glass tables show real-time data generated by KPIs and services.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/GTOverview The glass table editor is a tool that allows you to create and edit glass tables in ITSI. Some of the capabilities of the glass table editor are:
Creating glass tables from scratch or from existing templates.
Configuring service swapping on widgets to toggle displaying metrics from different services.
Adding KPI metric lanes to glass tables to show historical trends of KPI values.
The glass table editor does not support correlation search creation, which is a separate feature in ITSI that allows you to create searches that look for relationships between data points and generate notable events.
References: Overview of the glass table editor in ITSI, [Configure service swapping on glass tables], [Add KPI metric lanes to glass tables], [Overview of correlation searches in ITSI]

Among the anomaly detection algorithms included within Splunk IT Service Intelligence (ITSI), “Entity Cohesion” is a notable option. The Entity Cohesion algorithm is designed to detect anomalies by comparing the behavior of one entity against the collective behavior of a group of similar entities. This approach is particularly useful in scenarios where entities are expected to exhibit similar patterns of behavior under normal conditions. Anomalies are identified when an entity’s metrics deviate significantly from the group norm, suggesting a potential issue with that specific entity. This method leverages the concept of cohesion among similar entities to enhance the accuracy and relevance of anomaly detection within ITSI environments.

For Entity Cohesion anomaly detection in Splunk IT Service Intelligence (ITSI), the minimum number of entities a KPI must be split by is 2. Entity Cohesion as a method of anomaly detection focuses on identifying anomalies based on the deviation of an entity’s behavior in comparison to other entities within the same group or cohort. By requiring a minimum of only two entities, ITSI allows for the comparison of entities to detect significant deviations in one entity’s performance or behavior, which could indicate potential issues. This method leverages the idea that entities performing similar functions or within the same service should exhibit similar patterns of behavior, and significant deviations could be indicative of anomalies. The low minimum requirement of two entities ensures that this powerful anomaly detection feature can be utilized even in smaller environments.

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

It’s a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/EA/EpisodeOverview An episode is a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation. An episode helps you reduce alert noise and focus on the most important issues affecting your IT services. An episode is created by an aggregation policy, which is a set of rules that determines how to group notable events based on certain criteria, such as severity, source, title, and so on.
You can use episode review to view, manage, and resolve episodes in ITSI. The statement that defines an episode is:
C). A notable event group. This is true because an episode is composed of one or more notable events that are related by some common factor.
The other options are not definitions of an episode because:
A). A workflow task. This is not true because a workflow task is an action that you can perform on an episode, such as assigning an owner, changing the status, adding comments, and so on.
B). A deep dive. This is not true because a deep dive is a dashboard that allows you to analyze the historical trends and anomalies of your KPIs and metrics in ITSI.
D). A notable event. This is not true because a notable event is an alert generated by ITSI based on certain conditions or correlations, not a group of alerts.
References: [Overview of Episode Review in ITSI], [Overview of aggregation policies in ITSI]