[Jun-2026] SPLK-3002 Dumps PDF - SPLK-3002 Real Exam Questions Answers
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Splunk SPLK-3002 certification exam is an excellent opportunity for IT professionals to demonstrate their expertise in ITSI and acquire a recognized certification in this field. By passing SPLK-3002 exam, individuals can enhance their career prospects and contribute to the success of their organizations by ensuring that IT services are monitored and managed effectively.
Becoming a Splunk IT Service Intelligence Certified Admin through passing the SPLK-3002 exam demonstrates a high level of expertise and proficiency in using Splunk ITSI to monitor and analyze IT services and infrastructure. Splunk IT Service Intelligence Certified Admin certification is recognized and valued by IT organizations, as it validates the skills required to manage and troubleshoot complex IT environments effectively. Splunk IT Service Intelligence Certified Admin certification also provides an opportunity for IT professionals to advance their career and increase their earning potential.
Splunk SPLK-3002 exam is an essential certification for IT professionals who want to demonstrate their expertise in Splunk IT Service Intelligence. Splunk IT Service Intelligence Certified Admin certification provides IT professionals with a competitive edge in the job market and demonstrates their commitment to keeping up with the latest technologies and trends in IT operations and analytics. IT professionals can take advantage of a range of resources to prepare for the exam, including online training courses, practice exams, and study guides.
NEW QUESTION # 44
Which of the following is an advantage of using adaptive time thresholds?
- A. Automatically adjust KPI calculation to manage dynamic event data.
- B. Automatically adjust correlation search thresholds to adjust sensitivity over time.
- C. Automatically adjust aggregation policy grouping to manage escalating severity.
- D. Automatically update thresholds daily to manage dynamic changes to KPI values.
Answer: D
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.
References: Create adaptive KPI thresholds in ITSI
NEW QUESTION # 45
Which index contains ITSI Episodes?
- A. itsi_notable_archive
- B. itsi_summary
- C. itsi_grouped_alerts
- D. itsi_tracked_alerts
Answer: C
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/IndexOverview B is the correct answer because ITSI episodes are stored in the itsi_grouped_alerts index. This index contains notable events that have been grouped together based on predefined aggregation policies. Episodes help you reduce alert noise and focus on resolving incidents faster. References: [Overview of episodes in ITSI]
NEW QUESTION # 46
Besides creating notable events, what are the default alert actions a correlation search can execute? (Choose all that apply.)
- A. Ping a host.
- B. Send email.
- C. Run a script.
- D. Include in RSS feed.
Answer: B,C,D
Explanation:
Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/EA/ConfigCS B, C, and D are correct answers because they are the default alert actions that a correlation search can execute besides creating notable events. You can configure a correlation search to send an email, include the results in an RSS feed, or run a custom script when the search matches a defined pattern. Ping a host is not a default alert action for correlation searches. References: Configure correlation search settings in ITSI
NEW QUESTION # 47
When deploying ITSI on a distributed Splunk installation, which component must be installed on the search head(s)?
- A. ITSI app
- B. All ITSI components
- C. SA-ITSI-Licensechecker
- D. SA-ITOA
Answer: C
Explanation:
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.
NEW QUESTION # 48
After ITSI is initially deployed for the operations department at a large company, another department would like to use ITSI but wants to keep their information private from the operations group. How can this be achieved?
- A. Create service templates for each group and create the services from the templates.
- B. Create services for each group and set the permissions of the services to restrict them to each group.
- C. Create teams for each department and assign KPIs to each team.
- D. Create teams for each department and assign services to the teams.
Answer: D
Explanation:
In Splunk IT Service Intelligence (ITSI), creating teams for each department and assigning services to those teams is an effective way to segregate data and ensure that information remains private between different groups within an organization. Teams in ITSI provide a mechanism for role-based access control, allowing administrators to define which users or groups have access to specific services, KPIs, and dashboards. By setting up teams corresponding to each department and then assigning services to these teams, ITSI can accommodate multi-departmental use within the same instance while maintaining strict access controls. This ensures that each department can only view and interact with the data and services relevant to their operations, preserving confidentiality and data integrity across the organization.
NEW QUESTION # 49
When creating a custom deep dive, what color are services/KPIs in maintenance mode within the topology view?
- A. Blue
- B. Gray
- C. Gear Icon
- D. Purple
Answer: B
Explanation:
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. References: Deep Dives
NEW QUESTION # 50
Helga has a web service that depends on the database service to provide her website. She configures the database's "Heartbeat" KPI to be a dependency in the web service. When viewing the services in the Service Analyzer tree#view she sees a dotted line between the database service and the web service.
What is the meaning of the dotted line and how can Helga fix it?
- A. There is a cyclic dependency between the two services. Helga needs to add additional dependencies to change the dotted line to a solid line.
- B. There is a cyclic dependency between the two services. Helga needs to make sure that database service doesn't have any erroneous dependencies.
- C. The "Heartbeat" KPI is not currently affecting the web service health score. Helga needs to make sure the web service KPIs' importance are all set to 11.
- D. The "Heartbeat" KPI is not currently affecting the web service health score. Helga needs to make sure the Heartbeat KPI importance value is set to 0.
Answer: B
Explanation:
In Splunk IT Service Intelligence (ITSI), the Service Analyzer visually represents service dependencies. A solidline between services indicates a normal one#way dependency where one service's health contributes to another's service health score. However, adottedline signifies acyclic dependency, meaning that two services are defined as depending on each other in a loop. This typically happens when a service is configured to depend on another service that, directly or indirectly, also depends back on the first service. In Helga's scenario, because the web service is set to depend on the database using the Heartbeat KPI, and the configuration somehow established a reverse dependency (even inadvertently), the Service Analyzer shows the relationship as cyclic with a dotted line. To resolve this, Helga needs to check the service dependency configuration for the database service and ensure it doesnotmistakenly include the web service (or any chain of dependencies that leads back to it). Removing or correcting that erroneous dependency breaks the cycle, which will then change the representation to a solid line and properly reflect the dependency without circular references. It's not related to KPI importance values in this context - importance affects health score calculation but does not cause a cyclic dependency indicator.
NEW QUESTION # 51
What happens when an anomaly is detected?
- A. An anomaly alert will appear as a notable event in Episode Review.
- B. An anomaly alert will appear in core splunk, in index=main.
- C. A separate correlation search needs to be created in order to see it.
- D. A SNMP trap will be sent.
Answer: A
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.
NEW QUESTION # 52
Which index contains ITSI Episodes?
- A. itsi_notable_archive
- B. itsi_summary
- C. itsi_tracked_alerts
- D. itsi_grouped_alerts
Answer: A
NEW QUESTION # 53
Which of the following is a problem requiring correction in ITSI?
- A. Twoormore entitieswiththe same service ID.
- B. Twoormore entitieswiththe same entity key value inanyinfo field.
- C. Twoormore entitieswiththe same value in a single alias field.
- D. Twoormore entitieswiththe same entity ID.
Answer: C
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.
NEW QUESTION # 54
Which of the following describes a realistic troubleshooting workflow in ITSI?
- A. Service Analyzer -> Aggregation Policy -> Deep Dive
- B. Correlation Search -> Deep Dive -> Notable Event
- C. Service Analyzer -> Notable Event Review -> Deep Dive
- D. Correlation search -> KPI -> Aggregation Policy
Answer: C
Explanation:
A realistic troubleshooting workflow in ITSI is:
B) Service Analyzer -> Notable Event Review -> Deep Dive
This workflow involves using the Service Analyzer dashboard to monitor the health and performance of your services and KPIs, using the Notable Event Review dashboard to investigate and manage the notable events generated by ITSI, and using the Deep Dive dashboard to analyze the historical trends and anomalies of your KPIs and metrics.
The other workflows are not realistic because they involve components that are not part of the troubleshooting process, such as correlation search, aggregation policy, and KPI. These components are used to create and configure the alerts and episodes that ITSI generates, not to investigate and resolve them. Reference: [Service Analyzer dashboard in ITSI], Overview of Episode Review in ITSI, [Overview of deep dives in ITSI]
NEW QUESTION # 55
What should be considered when onboarding data into a Splunk index, assuming that ITSI will need to use this data?
- A. Check if the data could leverage pre-built KPIs from modules, then use the correct TA to onboard the data.
- B. Plan to build as many data models as possible for ITSI to leverage
- C. Make sure that all fields conform to CIM, then use the corresponding module to import related services.
- D. Use | stats functions in custom fields to prepare the data for KPI calculations.
Answer: A
Explanation:
Reference: https://newoutlook.it/download/book/splunk/advanced-splunk.pdf When onboarding data into a Splunk index, assuming that ITSI will need to use this data, you should consider the following:
B). Check if the data could leverage pre-built KPIs from modules, then use the correct TA to onboard the data.
This is true because modules are pre-packaged sets of services, KPIs, and dashboards that are designed for specific types of data sources, such as operating systems, databases, web servers, and so on. Modules help you quickly set up and monitor your IT services using best practices and industry standards. To use modules, you need to install and configure the correct technical add-ons (TAs) that extract and normalize the data fields required by the modules.
The other options are not things you should consider because:
A). Use | stats functions in custom fields to prepare the data for KPI calculations. This is not true because using
| stats functions in custom fields can cause performance issues and inaccurate results when calculating KPIs.
You should use | stats functions only in base searches or ad hoc searches, not in custom fields.
C). Make sure that all fields conform to CIM, then use the corresponding module to import related services.
This is not true because not all modules require CIM-compliant data sources. Some modules have their own data models and field extractions that are specific to their data sources. You should check the documentation of each module to see what data requirements and dependencies they have.
D). Plan to build as many data models as possible for ITSI to leverage. This is not true because building too many data models can cause performance issues and resource consumption in your Splunk environment. You should only build data models that are necessary and relevant for your ITSI use cases.
References: Overview of modules in ITSI, [Install technical add-ons for ITSI modules]
NEW QUESTION # 56
Which of the following are the default ports that must be configured on Splunk to use ITSI?
- A. SplunkWeb (8089), SplunkD (8088), and HTTP Collector (8000)
- B. SplunkWeb (8088), SplunkD (8089), and HTTP Collector (8000)
- C. SplunkWeb (8000), SplunkD (8089), and HTTP Collector (8088)
- D. SplunkWeb (8405), SplunkD (8519), and HTTP Collector (8628)
Answer: C
Explanation:
Reference: https://splunk.github.io/docker-splunk/ARCHITECTURE.html
C is the correct answer because ITSI uses the default ports of Splunk Enterprise for its communication and data collection. SplunkWeb uses port 8000, SplunkD uses port 8089, and HTTP Event Collector uses port
8088. These ports can be changed if needed, but they must match the configuration of Splunk Enterprise.
References: Ports used by ITSI
NEW QUESTION # 57
Which of the following is a good use case for a Multi-KPI alert?
- A. Alerting when the values of two or more KPIs go into maintenance mode.
- B. Alerting when two or more KPIs are deviating from their typical pattern.
- C. Alerting when the trend of two or more KPIs indicates service failure is imminent.
- D. Alerting when comparing the values of two or more KPIs indicates an unusual condition is occurring.
Answer: D
Explanation:
A Multi-KPI alert in Splunk IT Service Intelligence (ITSI) is designed to trigger based on the conditions of multiple Key Performance Indicators (KPIs). This type of alert is particularly useful when a single KPI's state is not sufficient to indicate an issue, but the correlation between multiple KPIs can provide a clearer picture of an emerging problem. The best use case for a Multi-KPI alert is therefore when comparing the values of two or more KPIs indicates an unusual condition is occurring. This allows for more nuanced and context-rich alerting mechanisms that can identify complex issues not detectable by monitoring individual KPIs. This approach is beneficial in complex environments where the interplay between different performance metrics needs to be considered to accurately detect and diagnose issues.
NEW QUESTION # 58
Which of the following are the default ports that must be configured on Splunk to use ITSI?
- A. SplunkWeb (8089), SplunkD (8088), and HTTP Collector (8000)
- B. SplunkWeb (8088), SplunkD (8089), and HTTP Collector (8000)
- C. SplunkWeb (8000), SplunkD (8089), and HTTP Collector (8088)
- D. SplunkWeb (8405), SplunkD (8519), and HTTP Collector (8628)
Answer: C
Explanation:
Reference:
C is the correct answer because ITSI uses the default ports of Splunk Enterprise for its communication and data collection. SplunkWeb uses port 8000, SplunkD uses port 8089, and HTTP Event Collector uses port 8088. These ports can be changed if needed, but they must match the configuration of Splunk Enterprise. Reference: Ports used by ITSI
NEW QUESTION # 59
Which anomaly detection algorithm is included within ITSI?
- A. Standard deviation
- B. Infantile regression
- C. Entity cohesion
- D. Linear regression
Answer: C
Explanation:
Among the anomaly detection algorithms included within Splunk IT Service Intelligence (ITSI), "Entity Cohesion" is a notable option. The Entity Cohesion algorithm is designed to detect anomalies by comparing the behavior of one entity against the collective behavior of a group of similar entities. This approach is particularly useful in scenarios where entities are expected to exhibit similar patterns of behavior under normal conditions. Anomalies are identified when an entity's metrics deviate significantly from the group norm, suggesting a potential issue with that specific entity. This method leverages the concept of cohesion among similar entities to enhance the accuracy and relevance of anomaly detection within ITSI environments.
NEW QUESTION # 60
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