Splunk SPLK-3002 Practice Exams
Last updated on Oct 06,2026- Exam Code: SPLK-3002
- Exam Name: Splunk IT Service Intelligence Certified Admin Exam
- Certification Provider: Splunk
- Latest update: Oct 06,2026
Question #71
Which of the following is an advantage of using adaptive time thresholds?
- A . Automatically update thresholds daily to manage dynamic changes to KPI values.
- B . Automatically adjust KPI calculation to manage dynamic event data.
- C . Automatically adjust aggregation policy grouping to manage escalating severity.
- D . Automatically adjust correlation search thresholds to adjust sensitivity over time.
Correct Answer: A
A
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/TimePolicies
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.
Reference: Create adaptive KPI thresholds in ITSI
A
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/TimePolicies
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.
Reference: Create adaptive KPI thresholds in ITSI
Question #72
When deploying ITSI on a distributed Splunk installation, which component must be installed on the search head(s)?
- A . SA-ITOA
- B . ITSI app
- C . All ITSI components
- D . SA-ITSI-Licensechecker
Correct Answer: B
B
Explanation:
Install SA-ITSI-Licensechecker and SA-UserAccess on any license master in a distributed or search head cluster environment. If a search head in your environment is also a license master, the license master components are installed when you install ITSI on the search heads.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Install/InstallDD
When deploying ITSI on a distributed Splunk installation, the component that must be installed on the search head(s) is the ITSI app. The ITSI app contains the main features and functionality of ITSI, such as service creation and management, KPI configuration, glass table creation and editing, episode review, deep dives, and so on. The ITSI app also contains some add-ons that provide additional functionality, such as SA-ITOA (IT Operations Analytics), SA-UserAccess (User Access Management), and SA-Utils (Utility Functions). The ITSI app must be installed on the search head(s) because it handles the search management and presentation functions for ITSI.
Reference: Install IT Service Intelligence in a distributed environment
B
Explanation:
Install SA-ITSI-Licensechecker and SA-UserAccess on any license master in a distributed or search head cluster environment. If a search head in your environment is also a license master, the license master components are installed when you install ITSI on the search heads.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Install/InstallDD
When deploying ITSI on a distributed Splunk installation, the component that must be installed on the search head(s) is the ITSI app. The ITSI app contains the main features and functionality of ITSI, such as service creation and management, KPI configuration, glass table creation and editing, episode review, deep dives, and so on. The ITSI app also contains some add-ons that provide additional functionality, such as SA-ITOA (IT Operations Analytics), SA-UserAccess (User Access Management), and SA-Utils (Utility Functions). The ITSI app must be installed on the search head(s) because it handles the search management and presentation functions for ITSI.
Reference: Install IT Service Intelligence in a distributed environment
Question #73
What happens when an anomaly is detected?
- A . A separate correlation search needs to be created in order to see it.
- B . A SNMP trap will be sent.
- C . An anomaly alert will appear in core splunk, in index=main.
- D . An anomaly alert will appear as a notable event in Episode Review.
Correct Answer: D
D
Explanation:
When an anomaly is detected in Splunk IT Service Intelligence (ITSI), it typically generates a notable event that can be reviewed and managed in the Episode Review dashboard. The Episode Review is part of ITSI’s Event Analytics framework and serves as a centralized location for reviewing, annotating, and managing notable events, including those generated by anomaly detection. This process enables IT operators and analysts to efficiently identify, prioritize, and respond to potential issues highlighted by the anomaly alerts. The integration of anomaly alerts into the Episode Review dashboard streamlines the workflow for managing and investigating these alerts within the broader context of IT service management and operational intelligence.
D
Explanation:
When an anomaly is detected in Splunk IT Service Intelligence (ITSI), it typically generates a notable event that can be reviewed and managed in the Episode Review dashboard. The Episode Review is part of ITSI’s Event Analytics framework and serves as a centralized location for reviewing, annotating, and managing notable events, including those generated by anomaly detection. This process enables IT operators and analysts to efficiently identify, prioritize, and respond to potential issues highlighted by the anomaly alerts. The integration of anomaly alerts into the Episode Review dashboard streamlines the workflow for managing and investigating these alerts within the broader context of IT service management and operational intelligence.
Question #74
Which of the following can generate notable events?
- A . Through ad-hoc search results which get processed by adaptive thresholds.
- B . When two entity aliases have a matching value.
- C . Through scheduled correlation searches which link to their respective services.
- D . Manually selected using the Notable Event Review panel.
Correct Answer: C
C
Explanation:
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.
C
Explanation:
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.
Question #75
Anomaly detection can be enabled on which one of the following?
- A . KPI
- B . Multi-KPI alert
- C . Entity
- D . Service
Correct Answer: A
A
Explanation:
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.
Reference: Apply anomaly detection to a KPI in ITSI
A
Explanation:
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.
Reference: Apply anomaly detection to a KPI in ITSI
Question #75
Anomaly detection can be enabled on which one of the following?
- A . KPI
- B . Multi-KPI alert
- C . Entity
- D . Service
Correct Answer: A
A
Explanation:
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.
Reference: Apply anomaly detection to a KPI in ITSI
A
Explanation:
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.
Reference: Apply anomaly detection to a KPI in ITSI
Question #77
Which features allow for customization of the Service Analyzer view in ITSI? (Choose two)
- A . Color coding based on severity
- B . Drag and drop functionality
- C . Adjustable time windows
- D . Hardcoded query parameters
Correct Answer: A, C
Question #78
How can admins manually control groupings of notable events?
- A . Correlation searches.
- B . Multi-KPI alerts.
- C . notable_event_grouping.conf
- D . Aggregation policies.
Correct Answer: D
D
Explanation:
In Splunk IT Service Intelligence (ITSI), administrators can manually control the grouping of notable events using aggregation policies. Aggregation policies allow for the definition of criteria based on which notable events are grouped together. This includes configuring rules based on event fields, severity, source, or other event attributes. Through these policies, administrators can tailor the event grouping logic to meet the specific needs of their environment, ensuring that related events are grouped in a manner that facilitates efficient analysis and response. This feature is crucial for managing the volume of events and focusing on the most critical issues by effectively organizing related events into manageable groups.
D
Explanation:
In Splunk IT Service Intelligence (ITSI), administrators can manually control the grouping of notable events using aggregation policies. Aggregation policies allow for the definition of criteria based on which notable events are grouped together. This includes configuring rules based on event fields, severity, source, or other event attributes. Through these policies, administrators can tailor the event grouping logic to meet the specific needs of their environment, ensuring that related events are grouped in a manner that facilitates efficient analysis and response. This feature is crucial for managing the volume of events and focusing on the most critical issues by effectively organizing related events into manageable groups.
Question #79
Which of the following applies when configuring time policies for KPI thresholds?
- A . A person can only configure 24 policies, one for each hour of the day.
- B . They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00
- C . If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use it.
- D . It is possible for multiple time policies to overlap.
Correct Answer: B
B
Explanation:
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.
Reference: Create time-based static KPI thresholds in ITSI
B
Explanation:
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.
Reference: Create time-based static KPI thresholds in ITSI
Question #80
What are valid ITSI Glass Table editor capabilities? (Choose all that apply.)
- A . Creating glass tables.
- B . Correlation search creation.
- C . Service swapping configuration.
- D . Adding KPI metric lanes to glass tables.
Correct Answer: A, C, D
A, C, D
Explanation:
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.
Reference: 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]
A, C, D
Explanation:
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.
Reference: 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]