Splunk IT Service Intelligence Certified Admin SPLK-3002 Exam Practice Test

Answer : D

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.

Answer : A

When creating a custom deep dive, services or KPIs that are in maintenance mode are shown in gray color in the topology view. This indicates that they are not actively monitored and do not generate alerts or notable events. Reference:Deep Dives

Answer : D

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.

Answer : C

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.

Answer : A, B, C

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

Answer : A

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.

Answer : B

In Splunk IT Service Intelligence (ITSI), when a Key Performance Indicator (KPI) aggregate value is calculated, the tstats function is often called. The tstats function in Splunk is used for rapid statistical queries over large volumes of data, which is particularly useful in ITSI for efficiently calculating aggregate values of KPIs across potentially vast datasets. This function allows for quick aggregation and summarization of indexed data, which is essential for monitoring and analyzing the performance metrics that KPIs represent in ITSI. Unlike the stats command, which operates on already retrieved events, tstats works directly on indexed data, providing faster performance especially when dealing with high volumes of data typical in an IT environment. The tstats command is therefore fundamental in the backend processing of ITSI for calculating aggregate values of KPIs, enabling real-time and historical analysis of service health and performance.