Pass the Splunk IT Service Intelligence Certified Admin SPLK-3002 Questions and answers with ValidTests

In Splunk IT Service Intelligence (ITSI), when using multiple event management policies, it is important to understand that policy processing is applied in a defined order. This order is crucial because it determines how events are processed and aggregated, and which rules are applied to events first. The order of policies can be customized, allowing administrators to prioritize certain policies over others based on the specific needs and operational logic of their IT environment. This feature provides flexibility in event management, enabling more precise control over event processing and ensuring that the most critical events are handled according to the desiredprecedence. This structured approach to policy processing helps in maintaining the efficiency and effectiveness of event management within ITSI.

In Splunk IT Service Intelligence (ITSI), the Service Health Score is a metric that provides a quantifiable measure of the overall health and performance of a service. The score ranges from 0 to 100, with higher scores indicating better health. The range for a normal Service Health score category is typically from 80 to 100. Scores within this range suggest that the service is performing well, with no significant issues affecting its health. This categorization helps IT and business stakeholders quickly assess the operational status of their services, enabling them to focus on services that may require attention or intervention due to lower health scores.

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

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.

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.

Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).

When an episode warrants investigation, the analyst acknowledges the episode, which moves the status from New to In Progress.

In the context of Splunk IT Service Intelligence (ITSI), service dependencies allow for the modeling of relationships between services, where the health of one service (dependent) can affect the health of another (primary).

B.It is best practice to use the dependent service's built-in 'ServiceHealthScore' KPI to reflect impact to the primary service:Utilizing the 'ServiceHealthScore' KPI of a dependent service as part of the primary service's health calculation is a recommended practice. This approach ensures that changes in the health of the dependent service directly influence the primary service's overall health score, providing a more holistic view of service health within the IT environment.

C.Setting the dependent service KPI importance level will be treated as any other KPI in the primary service's health score:When a dependent service's KPI is incorporated into a primary service, the importance level assigned to this KPI is factored into the primary service's overall health score calculation just like any other KPI. This means that the impact of the dependent service on the primary service can be weighted according to the business significance of the relationship between the services.

The other options are not accurate representations of ITSI service dependencies. Changes in KPI importance levels do not break dependencies, and there is no restriction on configuring impactful dependent services to only one primary service, as dependencies can be complex and multi-layered across various services.