Splunk SPLK-5001 Exam Dumps & Practice Test Questions

Question 1

Which two ITSI features must be enabled for Services to inherit KPI threshold values automatically from Entity rules? (Choose 2.)

A. Adaptive Thresholding
B. KPI Base Searches
C. Service Templates
D. Team-specific Aggregation Policies
E. Entity Matching Rules

Correct Answer : A, E

Explanation:
In Splunk IT Service Intelligence (ITSI), KPI (Key Performance Indicator) threshold values are crucial for monitoring and alerting on service performance. In order for services to inherit KPI threshold values automatically from Entity rules, certain ITSI features must be enabled.

Adaptive Thresholding (A) is one of the key features that allow automatic adjustment of thresholds based on historical data and trends, providing a more dynamic and responsive approach to monitoring. This feature can significantly impact how KPI thresholds are inherited because it adjusts the threshold values based on real-time data patterns and entity behavior. Without adaptive thresholding, KPI values may need to be set manually, which can lead to inconsistency and missed alerts, especially when the environment is dynamic.

Entity Matching Rules (E) are critical because they define the relationships between entities and their associated KPIs. These rules ensure that the KPI thresholds defined for individual entities are correctly mapped and inherited by the associated services. If entity matching rules are not in place or are not configured correctly, the automatic inheritance of KPI thresholds won't function as intended. By matching entities with the correct service, these rules allow for seamless threshold inheritance, enabling better monitoring and more accurate detection of issues in services.

On the other hand, the other options are not directly related to automatic inheritance of KPI threshold values. While KPI Base Searches (B) are important for searching and retrieving data related to KPIs, they don't have a direct role in threshold inheritance. Similarly, Service Templates (C) and Team-specific Aggregation Policies (D) are used for organizing and managing services and team-specific policies, but they do not directly facilitate automatic threshold inheritance from entity rules.

Therefore, enabling A. Adaptive Thresholding and E. Entity Matching Rules is essential for ensuring that services can inherit KPI threshold values automatically from entity rules.

Question 2

In ITSI, which two correlation-search attributes determine whether a notable event creates a new episode or is added to an existing episode in Episode Review? (Choose 2.)

A. Time-based suppression window
B. Group-by fields
C. Drilling-down search string
D. Split-by field list
E. Episode title tokenization

Correct Answer : B, D

Explanation:
In Splunk IT Service Intelligence (ITSI), notable events are generated from correlation searches, and these events are organized into episodes. Episodes are collections of related notable events that help to identify and resolve incidents. Whether a new notable event creates a new episode or is added to an existing episode depends on several attributes configured in the correlation search.

Group-by fields (B) play a crucial role in determining whether a new episode is created or an existing episode is updated. These fields define the attributes that correlate events together, grouping them into the same episode. If the values of the group-by fields match those of an existing episode, the new notable event will be added to that episode. If there is no match, a new episode will be created. The group-by fields help ensure that similar events, with common attributes, are correctly bundled together into the same episode, facilitating easier incident investigation and resolution.

Split-by field list (D) is another critical attribute. It is used to break down and categorize events within a single episode based on specific field values. This allows correlation searches to assign events to different split-by categories within the same episode. If the field value in the split-by field list matches an existing value, the event is added to the corresponding split category of the existing episode. If no match exists, a new episode or split will be created. This attribute ensures that detailed distinctions within the same episode can be maintained while still associating related events under one overarching episode.

Other attributes, such as Time-based suppression window (A) and Episode title tokenization (E), are important for managing event suppression and defining the episode title, but they are not directly responsible for determining whether a new episode is created or an existing one is updated. The Drilling-down search string (C) is used for refining searches but does not affect the episode creation process directly.

Thus, the correlation-search attributes that influence whether a notable event creates a new episode or is added to an existing episode are B. Group-by fields and D. Split-by field list. These attributes help maintain a logical structure for episode management, making it easier to track and analyze related incidents.

Question 3

Which two data integrations can populate ITSI Entities without writing custom SPL? (Choose 2.)

A. Splunk Add-on for Amazon Web Services (AWS) with metadata lookup
B. ServiceNow CMDB integration using the ITSI Module for CMDB Sync
C. CSV file import through the itsi_entity_import command
D. Splunk Stream real-time wire-data ingestion
E. SNMP Trap data using the Splunk Add-on for SNMP

Answer: A, B

Explanation:
In Splunk IT Service Intelligence (ITSI), entities represent the key components of an IT infrastructure, such as servers, applications, or other systems. Populating these entities in ITSI typically requires data integrations that can automatically provide the necessary information without the need to write custom SPL (Search Processing Language) queries. Some data integrations are designed to simplify this process by using existing data sources and providing out-of-the-box functionality.

The Splunk Add-on for Amazon Web Services (AWS) with metadata lookup (A) is an excellent example of an integration that can automatically populate ITSI entities without custom SPL. This add-on provides the ability to collect AWS metadata, which includes information about instances, services, and other resources. By utilizing this metadata lookup, ITSI can automatically create and update entities based on the AWS data, which is critical for organizations using AWS services. This integration allows entities to be populated without the need for custom SPL, as it uses pre-configured lookups and field mappings.

Similarly, the ServiceNow CMDB integration using the ITSI Module for CMDB Sync (B) provides another effective method for automatically populating ITSI entities. ServiceNow's Configuration Management Database (CMDB) contains a detailed inventory of IT assets and their relationships. The ITSI Module for CMDB Sync facilitates the synchronization of this data with ITSI entities, ensuring that the IT infrastructure's configuration data is accurately reflected in ITSI without needing custom SPL queries. This integration allows ITSI to use ServiceNow CMDB data to define and manage entities, ensuring that the most up-to-date asset information is automatically populated.

The other options—CSV file import through the itsi_entity_import command (C), Splunk Stream real-time wire-data ingestion (D), and SNMP Trap data using the Splunk Add-on for SNMP (E)—either involve more complex configurations or may require some SPL customization. The CSV import method typically needs to be tailored to the specific entity data structure, while Splunk Stream and SNMP Trap data are more about data collection and might still require custom configurations to align the data with ITSI entities.

Therefore, the best options for populating ITSI entities without writing custom SPL are A. Splunk Add-on for Amazon Web Services (AWS) with metadata lookup and B. ServiceNow CMDB integration using the ITSI Module for CMDB Sync.

Question 4

Which two actions occur when you clone an existing Service Template in ITSI? (Choose 2.)

A. All linked Services automatically switch to the new clone
B. KPI Base Searches referenced by the template are duplicated
C. Acceleration settings are copied into the cloned template
D. An ACL identical to the source template is applied
E. Deep-Learning Assist predictive models are reset in the clone

Answer: B, C

Explanation:
In Splunk IT Service Intelligence (ITSI), cloning an existing Service Template allows users to create a new service template based on an existing one. This can be useful for creating similar service templates without having to manually configure each setting. When cloning a service template, certain elements and settings are carried over, while others may need to be manually adjusted or reset. Understanding the actions that take place when cloning a service template is essential for efficient service template management.

KPI Base Searches referenced by the template are duplicated (B) when cloning a service template. This ensures that the key performance indicators (KPIs) used by the original template are copied over into the new template. However, while the base searches themselves are copied, it’s important to check that the base searches still align with the new service structure or configuration in the cloned template. This action helps maintain consistency between the original template and its clone by automatically inheriting the same search logic.

Additionally, acceleration settings are copied into the cloned template (C). Acceleration settings are critical for performance optimization in ITSI, especially when dealing with large datasets or real-time data. When cloning a service template, the acceleration settings from the original template are carried over to the new template, ensuring that the cloned service performs similarly to the original service in terms of data indexing and search performance. This action simplifies the process of ensuring that performance tuning and acceleration settings are preserved when creating new service templates.

On the other hand, all linked Services automatically switch to the new clone (A) is not true. When a service template is cloned, the linked services do not automatically switch to the new clone. The linked services must be manually adjusted to use the new service template, as the relationship between services and templates is not automatically updated during cloning.

An ACL identical to the source template is applied (D) is also not true. Cloning a service template does not automatically copy over the Access Control List (ACL) settings. The ACL, which governs user access and permissions, must be set separately for the cloned template, ensuring that access control is configured appropriately for the new service.

Finally, Deep-Learning Assist predictive models are reset in the clone (E) is a common result of cloning a service template. Predictive models, particularly those powered by deep learning algorithms, may not be automatically transferred with the cloned template. These models typically need to be retrained or reconfigured for the cloned service template, as the data patterns or KPIs may differ from the original template.

Thus, the two actions that occur when cloning an existing service template are B. KPI Base Searches referenced by the template are duplicated and C. Acceleration settings are copied into the cloned template.

Question 5

To reduce search load, which two ITSI mechanisms allow you to re-use KPI data across multiple Services without re-executing identical base searches? (Choose 2.)

A. KPI Cloning with “Reuse Existing Base Search”
B. Shared Summary Index acceleration for KPI base searches
C. Back-fill acceleration using the collect command
D. Service Analyzer aggregation-policy caching
E. Global Threshold Templates referenced by KPI token substitution

Answer: A, B

Explanation:
To optimize the performance and reduce the search load in ITSI (IT Service Intelligence), there are a few mechanisms that allow for the reuse of KPI (Key Performance Indicator) data without the need to re-execute the same base searches.

• KPI Cloning with “Reuse Existing Base Search” (Option A) is a feature that allows the reuse of an already executed base search for different KPIs across multiple services. Instead of executing the same base search again for each KPI, it leverages the data from the first execution, improving efficiency and reducing search loads. By cloning a KPI and enabling the "Reuse Existing Base Search" option, ITSI ensures that no redundant searches are performed, thereby saving on system resources.

• Shared Summary Index acceleration for KPI base searches (Option B) allows for the acceleration of base searches by using a shared summary index. This mechanism aggregates search results into a summary index, which can then be accessed by multiple services and KPIs. It eliminates the need to repeatedly run the same complex base searches, significantly reducing search load. The summary index can be reused for different KPIs without having to re-execute the same underlying searches, making it an effective strategy for performance optimization.

In contrast, the other options listed are either unrelated to the reuse of KPI data or involve processes that do not directly affect search load reduction in the same way.

• Back-fill acceleration using the collect command (Option C) is primarily used for historical data collection and does not directly address the issue of reducing search load by reusing KPI data.

• Service Analyzer aggregation-policy caching (Option D) deals with the aggregation and caching of data within Service Analyzer but does not address the reuse of KPI search data across services.

• Global Threshold Templates referenced by KPI token substitution (Option E) involves defining threshold values but does not influence the reuse of base search data.

By using A and B, ITSI can minimize redundant searches and improve the overall performance when working with multiple services and KPIs.

Question 6

Which two options are available when you create an Adaptive Threshold rule for a KPI in ITSI? (Choose 2.)

A. Specify minimum number of training data points
B. Select percentile-based dynamic thresholds
C. Configure anomaly score sensitivity (High/Medium/Low)
D. Enable federated search execution on search head cluster peers
E. Apply seasonality detection for weekly patterns

Answer: A, B

Explanation:
When creating an Adaptive Threshold rule for a KPI (Key Performance Indicator) in ITSI (IT Service Intelligence), two key features allow for the fine-tuning and customization of how thresholds are set. These features help to dynamically adjust thresholds based on the behavior of the KPI data over time, improving the accuracy of anomaly detection and alerting.

• Specify minimum number of training data points (Option A) is important because adaptive thresholds rely on historical data to learn the normal behavior of a KPI. Specifying a minimum number of training data points ensures that the system has enough data to create a meaningful baseline for the KPI. This prevents thresholds from being set based on insufficient data, which could lead to inaccurate or unreliable results.

• Select percentile-based dynamic thresholds (Option B) is a feature that allows the adaptive threshold rule to set dynamic thresholds based on percentiles (e.g., 95th percentile) of the historical data. Instead of using static thresholds, this method enables the system to adjust the threshold dynamically as the KPI data evolves, ensuring that anomalies are detected in the context of the historical distribution of the data. This is particularly useful for KPIs with fluctuating or variable patterns over time.

Other options, while useful in certain contexts, are not directly related to the process of creating an adaptive threshold rule for a KPI.

• Configure anomaly score sensitivity (High/Medium/Low) (Option C) is more related to configuring the sensitivity of anomaly detection itself rather than the specific rules for setting adaptive thresholds. It adjusts how the system reacts to deviations but is not about defining the thresholds themselves.

• Enable federated search execution on search head cluster peers (Option D) involves search performance and resource distribution across a search head cluster and is not relevant to setting adaptive thresholds for KPIs.

• Apply seasonality detection for weekly patterns (Option E) is useful for identifying and adjusting for patterns in data that repeat over time (such as weekly cycles), but it does not directly pertain to the creation of adaptive thresholds themselves.

Therefore, A and B are the most relevant options for setting adaptive thresholds for KPIs in ITSI, as they both help tailor the thresholds based on historical data and dynamic patterns.

Question 7

When integrating ITSI with Splunk On-Call (VictorOps), which two configuration items are mandatory on the ITSI side? (Choose 2.)

A API key for the On-Call REST endpoint
B Modifying itsi_settings.conf to enable on-call dispatching
C Correlation search action type Send to VictorOps
D Webhook URL configured in global notifications
E Episode Review status mapping to On-Call incident states

Answer: A, C

Explanation:
When integrating ITSI (IT Service Intelligence) with Splunk On-Call (previously known as VictorOps), there are specific configuration items that are essential for the integration to function properly. These configurations enable ITSI episodes or alerts to trigger incidents in Splunk On-Call using webhooks and REST API interactions.

The integration works by forwarding alerts generated in ITSI (specifically from correlation searches or aggregation policies) to Splunk On-Call. To achieve this, certain key elements must be in place on the ITSI side:

Option A is correct because the API key is a mandatory credential required to authenticate ITSI with the Splunk On-Call REST endpoint. Without this API key, ITSI cannot communicate with Splunk On-Call’s API, which is necessary for sending incident information. This API key is configured when setting up the webhook connection to Splunk On-Call and is passed as a header or part of the request payload for authentication.

Option C is also correct because ITSI uses a correlation search action type called "Send to VictorOps" to dispatch incidents to Splunk On-Call. This is an action type available in ITSI that is explicitly designed for this integration. The correlation search identifies conditions (e.g., multiple KPIs violating thresholds) and then triggers this action to push incident data to Splunk On-Call. Without configuring this action type in the correlation search, alerts will not be transmitted, making it a required component.

Now, examining the incorrect options:

Option B is incorrect. While modifying configuration files like itsi_settings.conf might be necessary for advanced setups or debugging, it is not a mandatory step for enabling Splunk On-Call integration. ITSI provides UI-based configuration options for integrations, and the necessary settings can be completed through the GUI without direct file modifications in most standard use cases.

Option D is incorrect. Although webhook URLs are used in ITSI for many third-party integrations, VictorOps/Splunk On-Call integration specifically uses an API-based mechanism and the action type configured in correlation searches. There is no need to configure a webhook URL globally unless you're setting up a more generic or manual integration, which is not the default or mandatory method for VictorOps.

Option E is incorrect because Episode Review status mapping to On-Call incident states is not a required configuration. While mapping statuses between systems could enhance integration fidelity, it's not mandatory for the actual dispatching of incidents. ITSI can send incidents to Splunk On-Call regardless of whether a custom status mapping has been implemented.

In summary, to enable integration between ITSI and Splunk On-Call, you must authenticate using an API key and configure the correct correlation search action. These are the two indispensable components that drive the automation of incident creation in On-Call from ITSI-generated alerts.

Question 8

Which two ITSI capabilities rely on the KV Store collection service_kpi_alerts? (Choose 2.)

A Generating adaptive threshold baselines
B Driving real-time color changes in Service Analyzer glass tables
C Persisting KPI alert state for the Anomaly Swimlane
D Summarizing notable event counts for ITSI Overview dashboards
E Tracking KPI gap-filling for missing data points

Answer: B, C

Explanation:
In Splunk ITSI, KV Store collections play a critical role in maintaining stateful and historical data used across various features. One such KV Store collection, service_kpi_alerts, is central to storing real-time and recent alerting states of KPIs (Key Performance Indicators). This data is critical for visual displays and the operational state management of services.

Let’s break down what each option involves and how it relates to service_kpi_alerts.

Option B is correct. The Service Analyzer is a central UI component in ITSI that provides a real-time view of services and their KPIs. The color-coding of KPIs (red for critical, yellow for warning, green for normal, etc.) is driven by the most recent KPI alert states. These alert states are retrieved from the service_kpi_alerts KV Store, which ensures that the UI remains responsive and reflects the real-time health of services. Without this collection, the Service Analyzer would not be able to display accurate real-time KPI statuses.

Option C is also correct. The Anomaly Swimlane in Episode Review relies on the persisted KPI alert states to track anomalies over time. This historical KPI alert information is critical for visualizing when KPI thresholds were breached and how those breaches correlate with other events. These alert states are stored in service_kpi_alerts, making it essential for the swimlane’s operation. If this collection is not updated, the swimlane visualization would fail to render accurate anomaly data.

Now let’s consider why the other options are incorrect:

Option A is incorrect. Adaptive thresholding uses historical raw KPI data to generate dynamic baselines. This process leverages statistical models and time series data rather than the real-time alert state stored in service_kpi_alerts. Thus, adaptive threshold generation does not rely on this KV Store collection.

Option D is incorrect. While the ITSI Overview dashboards might summarize notable events, they primarily rely on notable event indexes and correlation search results rather than real-time KPI alert state stored in service_kpi_alerts. These dashboards are more focused on event aggregation than real-time KPI states.

Option E is incorrect. Gap-filling is a process related to time series completeness and uses data input and search logic to interpolate or impute missing KPI values. It does not rely on the service_kpi_alerts collection, which is more about alert state persistence than time series integrity.

In summary, the service_kpi_alerts KV Store is crucial for driving real-time visual updates in Service Analyzer and supporting anomaly visualization in the swimlane. These functionalities depend on knowing the current and recent alert states of KPIs, which this KV Store effectively manages.

Question 9

In a multisite search-head-cluster architecture, which two best practices ensure reliable propagation of ITSI objects (such as KPIs and Services) across peers? (Choose 2.)

A Configure captain-elected-only write mode for the KV Store
B Use the SHC deployer to push ITSI knowledge bundles
C Set conf_replication_include.itsi* in server.conf
D Enable KV-store-to-file replication for itsi_service_templates
E Disable automatic bundle replication for itsi_analytics indexes

Answer: A, C

Explanation:
In a multisite search-head cluster (SHC) architecture, ensuring consistent propagation of ITSI objects such as KPIs, services, service templates, and glass tables across all cluster members is vital. Since many ITSI configurations are stored in the KV Store, improper synchronization between members can lead to inconsistencies, data conflicts, or unexpected behaviors in dashboards and correlation searches. To mitigate this, Splunk recommends specific configurations that help maintain data integrity and prevent race conditions in distributed environments.

Option A is correct because enabling captain-elected-only write mode for the KV Store ensures that only the captain node in the SHC is allowed to perform write operations to the KV Store. This is crucial for ITSI because objects like KPIs, services, and thresholds are stored in KV Store collections. Without this mode, multiple nodes might attempt concurrent writes, leading to data conflicts. Enforcing this mode centralizes the write authority, ensures consistent updates, and reduces the risk of corruption or duplication across the cluster.

Option C is also correct. Setting the conf_replication_include.itsi* parameter in server.conf ensures that ITSI-specific configuration files (e.g., those prefixed with itsi) are included in the search-head cluster’s configuration replication mechanism. By default, not all custom or app-specific configuration files are replicated unless explicitly included. Ensuring these ITSI configurations are replicated guarantees that every cluster member has synchronized configuration files, which is necessary for consistent behavior across services and KPIs on all nodes.

Now let’s evaluate the incorrect options:

Option B is incorrect. The SHC deployer is used to distribute apps and configuration files to SHC members. However, ITSI is not designed to be deployed or updated via the SHC deployer, as it manages much of its configuration through the KV Store and internal app logic. Using the SHC deployer to push ITSI-related bundles can interfere with its internal mechanisms and may result in unexpected behavior or data loss.

Option D is incorrect. KV-store-to-file replication is a mechanism that helps export KV Store data to flat files, typically for backup or offline processing. While useful in certain contexts, enabling this specifically for collections like itsi_service_templates does not ensure peer-to-peer KV Store synchronization in an SHC environment. Instead, it is a data export mechanism and not a real-time consistency solution.

Option E is incorrect. Disabling automatic bundle replication for itsi_analytics indexes has no bearing on KV Store object propagation or ITSI configuration consistency. Index replication refers to the distribution of search-time knowledge bundles—not configuration or KV Store synchronization. Disabling such replication could even hinder performance in distributed searches.

In summary, ensuring reliable propagation of ITSI objects in a multisite SHC environment depends on enabling captain-only writes to the KV Store and configuring replication includes for ITSI-specific configurations. These measures align with Splunk best practices and are critical for maintaining a healthy and synchronized ITSI deployment across search-head cluster members.

Question 10

Which two statements about ITSI glass-table performance are true? (Choose 2.)

A Excessive use of real-time searches in glass-table panels can cause search-head CPU spikes
B Glass-table drill-down clicks trigger REST calls to the KPI REST endpoint, not ad-hoc SPL
C Setting the refresh interval to Off disables all underlying searches permanently
D Background images larger than 4 MB are automatically down-sampled by ITSI
E A glass-table clone resets all token-based color thresholds to default values

Answer: A, B

Explanation:
Glass tables in Splunk ITSI offer a dynamic, customizable way to visualize real-time health metrics and KPI data. While they provide great flexibility and rich visuals, their performance can be impacted by how the glass tables are designed—especially in large or complex environments. Performance considerations typically revolve around the types of searches used, frequency of refresh, image handling, and token logic.

Option A is correct. Using real-time searches in glass table panels can indeed lead to significant CPU utilization on the search head. Real-time searches maintain continuous search threads that constantly pull data as it comes in. When glass tables use many of these searches simultaneously—particularly in enterprise environments—this leads to high resource consumption, potentially causing search queuing, delays, or even impacting other users' search performance. Splunk generally advises avoiding real-time searches in glass tables unless absolutely necessary and instead recommends scheduled or accelerated searches to balance performance with responsiveness.

Option B is also correct. Drill-downs in glass tables that target KPIs often invoke REST API calls to endpoints such as /servicesNS/nobody/SA-ITOA/itoa_interface/kpi, instead of executing fresh ad-hoc SPL (Search Processing Language) searches. This design is intentional, as it allows the application to reuse KPI data already stored in the KV Store, reducing search load and speeding up UI responsiveness. This improves the glass table’s performance, especially when rendering complex drill-downs in real-time dashboards.

Now, analyzing the incorrect options:

Option C is incorrect. Setting the refresh interval to Off in a glass table disables auto-refreshing of data, but it does not permanently stop underlying searches. Manual interactions, such as reloading the page or triggering a token update, can still initiate data retrieval. It simply means that data will not refresh automatically on a schedule, helping conserve resources in passive viewing situations.

Option D is incorrect. While it is best practice to use optimized and smaller background images to ensure good performance, ITSI does not automatically down-sample background images larger than 4 MB. Instead, uploading large images may lead to longer load times or may fail depending on browser or system constraints, but there is no built-in automatic compression or resizing in ITSI’s glass table image handling.

Option E is incorrect. Cloning a glass table retains most of the token-based logic, including color thresholds and other configurations. These settings are copied over as part of the glass table’s JSON configuration. Users might need to adjust or validate tokens after cloning to align with new panel IDs or KPI mappings, but thresholds do not automatically reset.

In conclusion, understanding glass table performance hinges on how searches are structured and how data is retrieved. Avoiding real-time searches and recognizing that REST calls—not SPL—power drill-downs are two crucial insights for optimizing both system health and end-user experience.