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.

Creating a custom module in Splunk IT Service Intelligence (ITSI) is particularly beneficial for the purpose of migrating KPI base searches and related visualizations to other ITSI installations. Custom modules can encapsulate a set of configurations, searches, and visualizations that are tailored to specific monitoring needs or environments. By packaging these elements into a module, it becomes easier to transfer, deploy, and maintain consistency across different ITSI instances. This modularity supports the reuse of developed components, simplifying the process of scaling and replicating monitoring setups in diverse operational contexts. The ability to migrate these components seamlessly enhances operational efficiency and ensures that best practices and custom configurations can be shared across an organization's ITSI deployments.

An ITSI Glass Table provides a customizable, high-level view that can display a system's topology overlaid with real-time Key Performance Indicator (KPI) metrics and service health scores. This visualization tool allows users to create a visual representation of their IT infrastructure, applications, and services, integrating live data to monitor the health and performance of each component in context. The ability to overlay KPI metrics on the system topology enables IT and business stakeholders to quickly understand the operational status and health of various elements within their environment, facilitating more informed decision-making and rapid response to issues.

A is the correct answer because anomaly detection can be enabled on a KPI level in ITSI. Anomaly detection allows you to identify trends and outliers in KPI search results that might indicate an issue with your system. You can enable anomaly detection for a KPI by selecting one of the two anomaly detection algorithms in the KPI configuration panel. References:  Apply anomaly detection to a KPI in ITSI

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.

When planning and designing a service tree in Splunk ITSI, the focus is on understanding how services, components, and dependencies relate to each other so that ITSI can model service health, impact, and business relevance accurately. A technical diagram of the application and its interconnections provides direct insight into the components and dependency relationships that must be included in the service tree. A report of historical incidents and associated root causes offers valuable context for defining which components are critical, where failures have occurred, and how issues propagate , making it highly relevant to service modeling. A service topology from an IT Service Management (ITSM) tool also directly informs the modeling process by showing configured relationships between infrastructure and services , which can be imported or referenced to build accurate service trees. In contrast, an organizational chart of the company shows the reporting relationships and team structure within the organization — useful for human resource planning or escalation paths, but not directly useful for service tree design in ITSI. Organizational charts do not provide information about application components, their runtime connections, or how failures affect service delivery, making them the least useful material when designing a service tree for technical and service health purposes. Therefore, company org charts are generally not relevant to ITSI service modeling.