SPLK-3002 Practice Test

Explanation:
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.

Explanation: A deep dive is a dashboard that allows you to analyze the historical trends and anomalies of your KPIs and metrics in ITSI. A deep dive displays a timeline of events and swim lanes of data that you can customize and filter to investigate issues and perform root cause analysis. Some of the capabilities of deep dives are:, B. Visualizing one or more service KPIs values by time. This is true because you can add KPI swim lanes to a deep dive to show the values and severity levels of one or more KPIs over time. You can also compare KPIs from different services or entities using service swapping or entity splitting., C. Examining and comparing alert levels for KPIs in a service over time. This is true because you can add alert swim lanes to a deep dive to show the alert levels and counts for one or more KPIs over time. You can also drill down into the alert details and view the notable events associated with each alert., D. Comparing swim lane values for a slice of time. This is true because you can use the time range selector to zoom in or out of a specific time range in a deep dive. You can also use the time brush to select a slice of time and compare the swim lane values for that time period., The other option is not a capability of deep dives because:, A. Comparing a service’s notable events over a time period. This is not true because deep dives do not display notable events, which are alerts generated by ITSI based on certain conditions or correlations. Notable events are displayed in other dashboards, such as episode review or glass tables.

Explanation: In Splunk IT Service Intelligence (ITSI), entities represent infrastructure components, applications, or other elements that are monitored. Each entity is uniquely identified by its entity ID, and entities can be associated with one or more services through the concept of aliases. A problem arises when two or more entities have the same value in a single alias field because aliases are used to match events to entities in ITSI. If multiple entities share the same alias value, ITSI might incorrectly associate data with the wrong entity, leading to inaccurate monitoring and analytics. This scenario requires correction to ensure that each alias uniquely identifies a single entity, thereby maintaining the integrity of the monitoring and analysis process within ITSI. The uniqueness of service IDs, entity IDs, and entity key values in info fields is also important but does not typically present the same level of issue as duplicate values in an alias field.

Explanation: 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.

Explanation:
Service templates in Splunk IT Service Intelligence (ITSI) are designed to streamline the creation of services by providing pre-defined configurations:

B. Service templates contain KPIs and KPI thresholds:This allows for the standardized deployment of services with predefined performance indicators and their associated thresholds, ensuring consistency across similar services.

C. Service templates can contain specific or generic entity rules:These rules define how entities are associated with services created from the template, allowing for both broad and targeted applicability.

While service templates contain configurations for KPIs, thresholds, and entity rules, the ability to modify templates after services have been instantiated from them is limited. Changes to a template do not retroactively affect services already created from that template. Moreover, service templates do not inherently contain domain-specific dashboards or deep dives; these are created separately within ITSI.