SPLK-2002 Splunk Enterprise Certified Architect Questions and Answers

 Configurations from the deployer are merged into the SPLUNK_HOME/etc/apps/APP_HOME/local directory on the search head cluster member. The deployer distributes apps and other configurations to the search head cluster members in the form of a configuration bundle. The configuration bundle contains the contents of the SPLUNK_HOME/etc/shcluster/apps directory on the deployer. When a search head cluster member receives the configuration bundle, it merges the contents of the bundle into its own SPLUNK_HOME/etc/apps directory. The configurations in the local directory take precedence over the configurations in the default directory. The SPLUNK_HOME/etc/system/local directory is used for system-level configurations, not app-level configurations. The SPLUNK_HOME/etc/apps/search/default directory is used for the default configurations of the search app, not the configurations from the deployer.

In the context of splunkd.log events written to the _internal index, the field that identifies the specific log channel is the "channel" field. This information is confirmed by the Splunk Common Information Model (CIM) documentation, where "channel" is listed as a field name associated with Splunk Audit Logs​​.

According to the Splunk Enterprise Search Head Clustering (SHC) Deployer documentation, the “local_only” push mode is the correct option when deploying built-in apps. This mode ensures that the deployer only pushes configurations from the local directory of built-in Splunk apps (such as search, learned, or launcher) without overwriting or merging their default app configurations.

In an SHC environment, the deployer is responsible for distributing configuration bundles to all search head members. Each push can be executed in different modes depending on how the admin wants to handle the app directories:

full: Overwrites both default and local folders of all apps in the bundle.

merge_to_default: Merges configurations into the default folder (used primarily for custom apps).

local_only: Pushes only local configurations, preserving default settings of built-in apps (the safest method for core Splunk apps).

default only: Pushes only default folder configurations (rarely used and not ideal for built-in app updates).

Using the “local_only” mode ensures that default Splunk system apps are not modified, preventing corruption or overwriting of base configurations that are critical for Splunk operation. It is explicitly recommended for pushing Splunk-provided (built-in) apps like search, launcher, and user-prefs from the deployer to all SHC members.

References (Splunk Enterprise Documentation):

• Managing Configuration Bundles with the Deployer (Search Head Clustering)

• Deployer Push Modes and Their Use Cases

• Splunk Enterprise Admin Manual – SHC Deployment Management

• Best Practices for Maintaining Built-in Splunk Apps in SHC Environments

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk Enterprise documentation clarifies that none of the listed configurations are prohibited in production. Splunk allows the Cluster Manager to be colocated with the Monitoring Console in small deployments because both are management-plane functions and do not handle ingestion or search traffic. The documentation also states that the Search Head Cluster Deployer is not a runtime component and has minimal performance requirements, so it may be colocated with the Monitoring Console or Licensing Master when hardware resources permit.

Splunk also supports virtual machines for both search heads and indexers, provided they are deployed with dedicated CPU, storage throughput, and predictable performance. Splunk’s official hardware guidance specifies that while bare metal often yields higher performance, virtualized deployments are fully supported in production as long as sizing principles are met.

Because Splunk explicitly supports all four configurations under proper sizing and best-practice guidelines, there is no correct selection for “unsupported.” The question is outdated relative to current Splunk Enterprise recommendations.

The correct answer is C. Increase the parallellngestionPipelines attribute. This is an option in limits.conf that may provide performance benefits at the forwarding tier, as it allows the forwarder to process multiple data inputs in parallel1. The parallellngestionPipelines attribute specifies the number of pipelines that the forwarder can use to ingest data from different sources1. By increasing this value, the forwarder can improve its throughput and reduce the latency of data delivery1. The other options are not effective options to provide performance benefits at the forwarding tier. Option A, enabling the indexed_realtime_use_by_default attribute, is not recommended, as it enables the forwarder to send data to the indexer as soon as it is received, which may increase the network and CPU load and degrade the performance2. Option B, increasing the maxKBps attribute, is not a good option, as it increases the maximum bandwidth, in kilobytes per second, that the forwarder can use to send data to the indexer3. This may improve the data transfer speed, but it may also saturate the network and cause congestion and packet loss3. Option D, increasing the max_searches_per_cpu attribute, is not relevant, as it only affects the search performance on the indexer or search head, not the forwarding performance on the forwarder4. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Configure parallel ingestion pipelines 2: Configure real-time forwarding 3: Configure forwarder output 4: Configure search performance

Splunk configuration files are files that contain settings that control various aspects of Splunk behavior, such as data inputs, outputs, indexing, searching, clustering, and so on1. Troubleshooting Splunk configuration files involves identifying and resolving issues that affect the functionality or performance of Splunk due to incorrect or conflicting configuration settings. Some of the tools and methods that can help with troubleshooting Splunk configuration files are:

search.log: This is a file that contains detailed information about the execution of a search, such as the search pipeline, the search commands, the search results, the search errors, and the search performance2. This file can help troubleshoot issues related to search configuration, such as props.conf, transforms.conf, macros.conf, and so on3.

btool output: This is a command-line tool that displays the effective configuration settings for a given Splunk component, such as inputs, outputs, indexes, props, and so on4. This tool can help troubleshoot issues related to configuration precedence, inheritance, and merging, as well as identify the source of a configuration setting5.

diagnostic logs: These are files that contain information about the Splunk system, such as the Splunk version, the operating system, the hardware, the license, the indexes, the apps, the users, the roles, the permissions, the configuration files, the log files, and the metrics6. These files can help troubleshoot issues related to Splunk installation, deployment, performance, and health7.

Option A is the correct answer because crash logs are the least helpful in troubleshooting Splunk configuration files. Crash logs are files that contain information about the Splunk process when it crashes, such as the stack trace, the memory dump, and the environment variables8. These files can help troubleshoot issues related to Splunk stability, reliability, and security, but not necessarily related to Splunk configuration9.

A dedicated deployment server is a Splunk instance that manages the distribution of configuration updates and apps to a set of deployment clients, such as forwarders, indexers, or search heads. A dedicated deployment server should be used when there are more than 50 deployment clients, because this number exceeds the recommended limit for a non-dedicated deployment server. A non-dedicated deployment server is a Splunk instance that also performs other roles, such as indexing or searching. Using a dedicated deployment server can improve the performance, scalability, and reliability of the deployment process. Option C is the correct answer. Option A is incorrect because the number of search peers does not affect the need for a dedicated deployment server. Search peers are indexers that participate in a distributed search. Option B is incorrect because the number of apps to deploy does not affect the need for a dedicated deployment server. Apps are packages of configurations and assets that provide specific functionality or views in Splunk. Option D is incorrect because the number of server classes does not affect the need for a dedicated deployment server. Server classes are logical groups of deployment clients that share the same configuration updates and apps12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Aboutdeploymentserver 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Whentousedeploymentserver

In the deployment planning process, a person should identify who gets to see network data in the data policy definition step. This step involves defining the data access policies and permissions for different users and roles in Splunk. The deployment schedule step involves defining the timeline and milestones for the deployment project. The topology diagramming step involves creating a visual representation of the Splunk architecture and components. The data source inventory step involves identifying and documenting the data sources and types that will be ingested by Splunk

According to Splunk’s Enterprise Security (ES) Installation and Sizing Guide, the majority of processing and computational load generated by the Splunk App for Enterprise Security is concentrated on the Search Head(s).

This is because Splunk ES is built around a search-driven correlation model — it continuously runs scheduled correlation searches, data model accelerations, and notables generation jobs. These operations rely on the search head tier’s CPU, memory, and I/O resources rather than on indexers. ES also performs extensive data model summarization, CIM normalization, and real-time alerting, all of which are search-intensive operations.

While indexers handle data ingestion and indexing, they are not heavily affected by ES beyond normal search request processing. The Cluster Manager only coordinates replication and plays no role in search execution, and Heavy Forwarders serve as data collection or parsing points with minimal analytical load.

Splunk officially recommends deploying ES on a dedicated Search Head Cluster (SHC) to isolate its high CPU and memory demands from other workloads. For large-scale environments, horizontal scaling via SHC ensures consistent performance and stability.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Security Installation and Configuration Guide

• Search Head Sizing for Splunk Enterprise Security

• Enterprise Security Overview – Workload Distribution and Performance Impact

• Splunk Architecture and Capacity Planning for ES Deployments

Intermediate forwarders are forwarders that receive data from other forwarders and then send that data to indexers. They can be useful in some scenarios, such as when network bandwidth or security constraints prevent direct forwarding to indexers, or when data needs to be routed, cloned, or modified in transit. However, intermediate forwarders also introduce additional complexity and overhead to the data pipeline, which can affect the performance and reliability of data ingestion. Therefore, intermediate forwarders should be avoided when possible, and used only when there is a clear benefit or requirement for them. Some of the drawbacks of intermediate forwarders are:

They increase the number of hops and connections in the data flow, which can introduce latency and increase the risk of data loss or corruption.

They consume more resources on the hosts where they run, such as CPU, memory, disk, and network bandwidth, which can affect the performance of other applications or processes on those hosts.

They require additional configuration and maintenance, such as setting up inputs, outputs, load balancing, security, monitoring, and troubleshooting.

They can create data duplication or inconsistency if they are not configured properly, such as when using cloning or routing rules.

Some of the references that support this answer are:

Configure an intermediate forwarder, which states: “Intermediate forwarding is where a forwarder receives data from one or more forwarders and then sends that data on to another indexer. This kind of setup is useful when, for example, you have many hosts in different geographical regions and you want to send data from those forwarders to a central host in that region before forwarding the data to an indexer. All forwarder types can act as an intermediate forwarder. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”

Intermediate data routing using universal and heavy forwarders, which states: “This document outlines a variety of Splunk options for routing data that address both technical and business requirements. Overall benefits Using splunkd intermediate data routing offers the following overall benefits: … The routing strategies described in this document enable flexibility for reliably processing data at scale. Intermediate routing enables better security in event-level data as well as in transit. The following is a list of use cases and enablers for splunkd intermediate data routing: … Limitations splunkd intermediate data routing has the following limitations: … Increased complexity and resource consumption. splunkd intermediate data routing adds complexity to the data pipeline and consumes resources on the hosts where it runs. This can affect the performance and reliability of data ingestion and other applications or processes on those hosts. Therefore, intermediate routing should be avoided when possible, and used only when there is a clear benefit or requirement for it.”

Use forwarders to get data into Splunk Enterprise, which states: “The forwarders take the Apache data and send it to your Splunk Enterprise deployment for indexing, which consolidates, stores, and makes the data available for searching. Because of their reduced resource footprint, forwarders have a minimal performance impact on the Apache servers. … Note: You can also configure a forwarder to send data to another forwarder, which then sends the data to the indexer. This is called intermediate forwarding. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”

The splunk offline --enforce-counts command will permanently decommission a peer node operating in an indexer cluster. This command will remove the peer node from the cluster and delete its data. This command should be used when the peer node is no longer needed or is being replaced by another node. The splunk stop -f command will stop the Splunk service on the peer node, but it will not decommission it from the cluster. The splunk offline -f command will take the peer node offline, but it will not delete its data or enforce the replication and search factors. The splunk decommission --enforce-counts command is not a valid Splunk command. For more information, see Remove a peer node from an indexer cluster in the Splunk documentation.

 The following is a good practice for a search head cluster deployer: The deployer must be used to distribute non-replicable configurations to search head cluster members. Non-replicable configurations are the configurations that are not replicated by the search factor, such as the apps and the server.conf settings. The deployer is the Splunk server role that distributes these configurations to the search head cluster members, ensuring that they have the same configuration. The deployer does not only distribute configurations to search head cluster members when they “phone home”, as this would cause configuration inconsistencies and delays. The deployer does not distribute configurations to search head cluster members to be valid configurations, as this implies that the configurations are invalid without the deployer. The deployer does not only distribute configurations to search head cluster members with splunk apply shcluster-bundle, as this would require manual intervention by the administrator. For more information, see Use the deployer to distribute apps and configuration updates in the Splunk documentation.

The correct tasks that the cluster manager performs in an indexer cluster are A. Generates and maintains the list of primary searchable buckets, B. If Indexer Discovery is enabled, provides the list of available peer nodes to forwarders, and D. Distributes app bundles to peer nodes. According to the Splunk documentation1, the cluster manager is responsible for these tasks, as well as managing the replication and search factors, coordinating the replication and search activities, and providing a web interface for monitoring and managing the cluster. Option C, ensuring all peer nodes are always using the same version of Splunk, is not a task of the cluster manager, but a requirement for the cluster to function properly2. Therefore, option C is incorrect, and options A, B, and D are correct.

1: About the cluster manager 2: Requirements and compatibility for indexer clusters

The telnet and tcpdump tools can be leveraged to diagnose connection problems between an indexer and forwarder. The telnet tool can be used to test the connectivity and port availability between the indexer and forwarder. The tcpdump tool can be used to capture and analyze the network traffic between the indexer and forwarder. The splunk btool command can be used to check the configuration files of the indexer and forwarder, but it cannot diagnose the connection problems. The splunk btprobe command does not exist, and it is not a valid tool.

According to Splunk’s Search Scheduler and Job Management documentation, the scheduler.log file, located within the _internal index, records the execution of scheduled and saved searches. This log provides a detailed record of when each search is triggered, how long it runs, and its success or failure status.

Each time a scheduled search runs (for example, alerts, reports, or summary index searches), an entry is written to scheduler.log with fields such as:

sid (search job ID)

app (application context)

savedsearch_name (name of the saved search)

user (owner)

status (success, skipped, or failed)

run_time and result_count

By searching the _internal index for sourcetype=scheduler (or directly viewing scheduler.log), administrators can confirm whether a specific saved search executed as expected and diagnose skipped or delayed runs due to resource contention or concurrency limits.

Other internal logs serve different purposes:

metrics.log records performance metrics.

kvstore.log tracks KV Store operations.

btool.log does not exist — btool outputs configuration data to the console, not a log file.

Hence, scheduler.log is the definitive and Splunk-documented source for validating scheduled search activity.

References (Splunk Enterprise Documentation):

• Saved Searches and Alerts – Scheduler Operation Details

• scheduler.log Reference – Monitoring Scheduled Search Execution

• Monitoring Console: Search Scheduler Health Dashboard

• Troubleshooting Skipped or Delayed Scheduled Searches

According to Splunk Enterprise administrative documentation, the btool utility is used to troubleshoot configuration settings by merging and displaying effective configurations from all configuration files (system, app, and local levels). When diagnosing license master configuration issues on a search peer or any cluster node, Splunk recommends using the command that specifically lists the license-related stanzas from server.conf.

The correct command is:

splunk cmd btool server list license --debug

This command reveals all configuration parameters under the [license] stanza, including those related to license master connections such as master_uri, pass4SymmKey, and disabled flags. The --debug flag ensures full path tracing of each configuration file, making it easy to identify conflicting or missing parameters that cause communication or validation errors between a search peer and the license master.

Other commands listed, such as btool server list clustering, are meant for diagnosing cluster configuration issues (like replication or search factors), not licensing. The check --debug command only validates syntax and structure, not specific configuration errors tied to licensing. Therefore, the correct and Splunk-documented method for diagnosing license configuration problems on a search peer is to inspect the license stanza using the btool server list license --debug command.

References (Splunk Enterprise Documentation):

• btool Command Reference – Troubleshooting Configuration Issues

• server.conf – License Configuration Parameters

• Managing Distributed License Configurations in Search Peer Clusters

• Troubleshooting License Master and Peer Connectivity

The deployer distributes apps and non-search related and manual configuration file changes to the search head cluster members. The deployer does not bootstrap a clean Splunk install for a search head cluster, as this is done by the captain. The deployer also does not distribute runtime knowledge object changes made by users across the search head cluster, as this is done by the replication factor. For more information, see Use the deployer to distribute apps and configuration updates in the Splunk documentation.

The splunk edit licenser-localslave command is used to convert a Splunk instance to a license slave. This command will configure the Splunk instance to contact a license master and receive a license from it. This command should be used when the Splunk instance is part of a distributed deployment and needs to share a license pool with other instances. The splunk add licenses command is used to add a license to a Splunk instance, not to convert it to a license slave. The splunk list licenser-slaves command is used to list the license slaves that are connected to a license master, not to convert a Splunk instance to a license slave. The splunk list licenser-localslave command is used to list the license master that a license slave is connected to, not to convert a Splunk instance to a license slave. For more information, see Configure license slaves in the Splunk documentation.

Splunk’s KV Store management documentation specifies that when converting a static CSV lookup to a KV Store lookup, the lookup data is stored in a MongoDB-based collection defined in collections.conf. To ensure that the KV Store lookup is replicated and available across all search head cluster members, administrators must include the attribute replicate=true within the collections.conf file.

This configuration instructs Splunk to replicate the KV Store collection’s data to all members in the Search Head Cluster (SHC), enabling consistent access and reliability across the cluster. Without this attribute, the KV Store collection would remain local to a single search head, making it unavailable for automatic lookups performed by other members.

Here’s an example configuration snippet from collections.conf:

[customer_lookup]

replicate = true

field.name = string

field.age = number

The attribute repFactor=true (mentioned in Options A and D) is unrelated to KV Store behavior—it applies to index replication, not KV Store replication. Similarly, replicate=true in lookups.conf (Option B) has no effect, as KV Store replication is controlled exclusively via collections.conf.

Once properly configured, the lookup can be defined in transforms.conf and referenced in props.conf for automatic lookup functionality.

References (Splunk Enterprise Documentation):

• KV Store Collections and Configuration – collections.conf Reference

• Managing KV Store Data in Search Head Clusters

• Automatic Lookup Configuration Using KV Store

• Splunk Enterprise Admin Manual – Distributed KV Store Replication Settings

Increasing the search factor in the cluster will best address the requirement of high availability for searchable data. The search factor determines how many copies of searchable data are maintained by the cluster. A higher search factor means that more indexers can serve the data in case of a failure or a maintenance event. Increasing the replication factor will improve the availability of raw data, but not searchable data. Increasing the number of search heads or CPUs on the indexers will improve the search performance, but not the availability of searchable data. For more information, see Replication factor and search factor in the Splunk documentation.

According to the Splunk documentation1, the Splunk Deployer deploys app content to the etc/slave-apps/ directory on the search head cluster members by default. This directory contains the apps that the deployer distributes to the members as part of the configuration bundle. The other options are false because:

The etc/apps/ directory contains the apps that are installed locally on each member, not the apps that are distributed by the deployer2.

The etc/shcluster/ directory contains the configuration files for the search head cluster, not the apps that are distributed by the deployer3.

The etc/deploy-apps/ directory is not a valid Splunk directory, as it does not exist in the Splunk file system structure4.

The following statements describe licensing in a clustered Splunk deployment: Free licenses do not support clustering, and replicated data does not count against licensing. Free licenses are limited to 500 MB of daily indexing volume and do not allow distributed searching or clustering. To enable clustering, a license with a higher volume limit and distributed features is required. Replicated data is data that is copied from one peer node to another for the purpose of high availability and load balancing. Replicated data does not count against licensing, because it is not new data that is ingested by Splunk. Only the original data that is indexed by the peer nodes counts against licensing. Each cluster member does not require its own clustering license, because clustering licenses are shared among the cluster members. Cluster members must share the same license pool and license master, because the license master is responsible for distributing licenses to the cluster members and enforcing the license limits

The Splunk Enterprise Capacity Planning Manual states that running Splunk in a virtualized environment typically results in a performance reduction of approximately 20% to 45% compared to equivalent deployments on physical hardware.

This degradation is primarily due to the virtualization overhead inherent in hypervisor environments (such as VMware, Hyper-V, or KVM), which can affect:

Disk I/O throughput and latency — the most critical factor for indexers.

CPU scheduling efficiency, particularly for multi-threaded indexing processes.

Network latency between clustered components.

Splunk’s documentation strongly emphasizes that while virtualized environments offer operational flexibility, they cannot match bare-metal performance, especially under heavy indexing loads.

To mitigate performance loss, Splunk recommends:

Reserving dedicated CPU and I/O resources for Splunk VMs.

Avoiding over-commitment of hardware resources.

Using high-performance SSD storage or paravirtualized disk controllers.

These optimizations can narrow the performance gap, but a 20–45% reduction remains a realistic expectation under typical conditions.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Capacity Planning Manual – Virtualization Performance Considerations

• Splunk on Virtual Infrastructure – Best Practices and Performance Tuning

• Indexer and Search Head Hardware Recommendations

• Performance Testing Guidelines for Splunk Deployments

 The Splunk instance with the given settings in SPLUNK_HOME/etc/system/local/server.conf is missing the master_uri attribute and needs to be restarted. The master_uri attribute is required for the master node to communicate with the peer nodes and the search head cluster. The master_uri attribute specifies the host name and port number of the master node. Without this attribute, the master node cannot function properly. The Splunk instance also needs to be restarted for the changes in the server.conf file to take effect. The replication_factor setting determines how many copies of each bucket are maintained across the peer nodes. The search factor is a separate setting that determines how many searchable copies of each bucket are maintained across the peer nodes. The search factor is not specified in the given settings, so it defaults to the same value as the replication factor, which is 2. This is not a multi-site cluster, because the site attribute is not specified in the clustering stanza. A multi-site cluster is a cluster that spans multiple geographic locations, or sites, and has different replication and search factors for each site.

A monitored log file is changing on the forwarder, but Splunk searches are not finding any new data that has been added. This could be caused by two possible reasons: B. An admin has removed the Splunk fishbucket on the forwarder. C. The last 256 bytes of the monitored file are not changing. Option B is correct because the Splunk fishbucket is a directory that stores information about the files that have been monitored by Splunk, such as the file name, size, modification time, and CRC checksum. If an admin removes the fishbucket, Splunk will lose track of the files that have been previously indexed and will not index any new data from those files. Option C is correct because Splunk uses the CRC checksum of the last 256 bytes of a monitored file to determine if the file has changed since the last time it was read. If the last 256 bytes of the file are not changing, Splunk will assume that the file is unchanged and will not index any new data from it. Option A is incorrect because running the splunk clean eventdata -index  command on the indexer will delete all the data from the specified index, but it will not affect the forwarder’s ability to send new data to the indexer. Option D is incorrect because Splunk does not use the first 256 bytes of a monitored file to determine if the file has changed12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Data/Monitorfilesanddirectories 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket

 The following statements are true regarding Splunk Enterprise performance:

Adding search peers increases the search throughput as search load increases. This is because adding more search peers distributes the search workload across more indexers, which reduces the load on each indexer and improves the search speed and concurrency.

Adding search heads provides additional CPU cores to run more concurrent searches. This is because adding more search heads increases the number of search processes that can run in parallel, which improves the search performance and scalability. The following statements are false regarding Splunk Enterprise performance:

Adding search peers does not increase the maximum size of search results. The maximum size of search results is determined by the maxresultrows setting in the limits.conf file, which is independent of the number of search peers.

Adding RAM to an existing search head does not provide additional search capacity. The search capacity of a search head is determined by the number of CPU cores, not the amount of RAM. Adding RAM to a search head may improve the search performance, but not the search capacity. For more information, see Splunk Enterprise performance in the Splunk documentation.

The following security option must be explicitly configured, as it is not enabled by default:

Certificate authentication between forwarders and indexers. This option allows the forwarders and indexers to verify each other’s identity using SSL certificates, which prevents unauthorized data transmission or spoofing attacks. This option is not enabled by default, as it requires the administrator to generate and distribute the certificates for the forwarders and indexers. For more information, see [Secure the communication between forwarders and indexers] in the Splunk documentation. The following security options are enabled by default:

Data encryption between Splunk Web and splunkd. This option encrypts the communication between the Splunk Web interface and the splunkd daemon using SSL, which prevents data interception or tampering. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [About securing Splunk Enterprise with SSL] in the Splunk documentation.

Certificate authentication between Splunk Web and search head. This option allows the Splunk Web interface and the search head to verify each other’s identity using SSL certificates, which prevents unauthorized access or spoofing attacks. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [About securing Splunk Enterprise with SSL] in the Splunk documentation.

Data encryption for distributed search between search heads and indexers. This option encrypts the communication between the search heads and the indexers using SSL, which prevents data interception or tampering. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [Secure your distributed search environment] in the Splunk documentation.

Splunk Enterprise officially requires a minimum of three search heads and one deployer for a supported Search Head Cluster (SHC) configuration. This ensures both high availability and data consistency within the cluster.

The Splunk documentation explains that a search head cluster uses RAFT-based consensus to elect a captain responsible for managing configuration replication, scheduling, and user workload distribution. The RAFT protocol requires a quorum of members to maintain consistency. In practical terms, this means a minimum of three members (search heads) to achieve fault tolerance — allowing one member to fail while maintaining operational stability.

The deployer is a separate Splunk instance responsible for distributing configuration bundles (apps, settings, and user configurations) to all members of the search head cluster. The deployer is not part of the SHC itself but is mandatory for its proper management.

Running with fewer than three search heads or replacing the deployer with a Deployment Server (as in Options B, C, or D) is unsupported and violates Splunk best practices for SHC resiliency and management.

References (Splunk Enterprise Documentation):

• Search Head Clustering Overview – Minimum Supported Architecture

• Deploy and Configure the Deployer for a Search Head Cluster

• High Availability and Fault Tolerance with RAFT in SHC

According to the Indexer Clustering Administration Guide, the most efficient and Splunk-recommended way to configure and manage receiving ports for all clustered indexers (peer nodes) is through the Cluster Manager (previously known as the Master Node).

In a clustered environment, configuration changes that affect all peer nodes—such as receiving port definitions—should be managed centrally. The correct procedure is to define the inputs configuration file (inputs.conf) within the Cluster Manager’s manager-apps directory. Specifically, the configuration is placed in:

$SPLUNK_HOME/etc/manager-apps/_cluster/local/inputs.conf

and then deployed to all peers using the configuration bundle push mechanism.

This centralized approach ensures consistency across all peer nodes, prevents manual configuration drift, and allows Splunk to maintain uniform ingestion behavior across the cluster.

Running splunk enable listen on each peer (Option C) or manually configuring inputs via Splunk Web (Option A) introduces inconsistencies and is not recommended in clustered deployments. Using the deployment-apps path (Option B) is meant for deployment servers, not for cluster management.

References (Splunk Enterprise Documentation):

• Indexer Clustering: Configure Peer Nodes via Cluster Manager

• Deploy Configuration Bundles from the Cluster Manager

• inputs.conf Reference – Receiving Data Configuration

• Splunk Enterprise Admin Manual – Managing Clustered Indexers

In search head clustering, there are two methods to transfer captaincy to a different member. One method is to use the Search Head Clustering settings menu from Splunk Web on any member. This method allows the user to select a specific member to become the new captain, or to let Splunk choose the best candidate. The other method is to run the splunk transfer shcluster-captain command from the member that the user wants to become the new captain. This method requires the user to know the name of the target member and to have access to the CLI of that member. Using the Monitoring Console is not a method to transfer captaincy, because the Monitoring Console does not have the option to change the captain. Running the splunk transfer shcluster-captain command from the current captain is not a method to transfer captaincy, because this command will fail with an error message

The deployer is a Splunk Enterprise instance that distributes apps and other configurations to the members of a search head cluster1.

The deployer cannot share functionality with any other Splunk Enterprise instance, including the license master, the master node, or the monitoring console2.

However, the search head cluster members can share functionality with the master node and the monitoring console, as long as they are not designated as the captain of the cluster3.

Therefore, the correct answer is B. License master, as it is the only instance that cannot share functionality with the deployer under any circumstances.

 The Monitoring Console is the Splunk tool that offers a health check for administrators to evaluate the health of their Splunk deployment. The Monitoring Console provides dashboards and alerts that show the status and performance of various Splunk components, such as indexers, search heads, forwarders, license usage, and search activity. The Monitoring Console can also run health checks on the deployment and identify any issues or recommendations. The btool is a command-line tool that shows the effective settings of the configuration files, but it does not offer a health check. The DiagGen is a tool that generates diagnostic snapshots of the Splunk environment, but it does not offer a health check. The SPL Clinic is a tool that analyzes and optimizes SPL queries, but it does not offer a health check. For more information, see About the Monitoring Console in the Splunk documentation.

To activate replication for an index in an indexer cluster, the repFactor attribute must be configured in indexes.conf on all peer nodes. This attribute specifies the replication factor for the index, which determines how many copies of raw data are maintained by the cluster. Setting the repFactor attribute to auto will enable replication for the index. The replicate attribute in indexes.conf is not a valid Splunk attribute. The repFactor attribute in outputs.conf and the replicate attribute in deploymentclient.conf are not related to replication for an index in an indexer cluster. For more information, see Configure indexes for indexer clusters in the Splunk documentation.

Splunk provides various methods to change the internal logging levels in a Splunk environment to troubleshoot an issue. All of the options are valid ways to do so. Option A is correct because the Monitoring Console (MC) allows the administrator to view and modify the logging levels of various Splunk components through a graphical interface. Option B is correct because the Splunk command line provides the splunk set log-level command to change the logging levels of specific components or categories. Option C is correct because the Splunk Web provides the Settings > Server settings > Server logging page to change the logging levels of various components through a web interface. Option D is correct because the log-local.cfg file allows the administrator to manually edit the logging levels of various components by overriding the default settings in the log.cfg file123

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Enabledebuglogging 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Serverlogging 3: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Loglocalcfg

Number of concurrent users: This is an important factor because it affects the search performance and resource utilization of the Splunk environment. More users mean more concurrent searches, which require more CPU, memory, and disk I/O. The number of concurrent users also determines the search head capacity and the search head clustering configuration12

Volume of incoming data: This is another crucial factor because it affects the indexing performance and storage requirements of the Splunk environment. More data means more indexing throughput, which requires more CPU, memory, and disk I/O. The volume of incoming data also determines the indexer capacity and the indexer clustering configuration13

Existence of premium apps: This is a relevant factor because some premium apps, such as Splunk Enterprise Security and Splunk IT Service Intelligence, have additional requirements and recommendations for the Splunk environment. For example, Splunk Enterprise Security requires a dedicated search head cluster and a minimum of 12 CPU cores per search head. Splunk IT Service Intelligence requires a minimum of 16 CPU cores and 64 GB of RAM per search head45

The Splunk Capacity Planning Manual defines the total required storage for indexes as a function of daily ingest rate, compression factor, retention period, and an additional padding buffer for index management and growth.

The formula is:

Storage = (Daily Data * Compression Factor * Retention Days) + Padding

Given the values:

Daily rate = 20 GB

Compression factor = 0.5 (50% reduction)

Retention period = 30 days

Padding = 100 GB

Plugging these into the formula gives:

20 * 0.5 * 30 + 100 = 400 GB

This result represents the estimated storage needed to retain 30 days of compressed indexed data with an additional buffer to accommodate growth and Splunk’s bucket management overhead.

Compression factor values typically range between 0.5 and 0.7 for most environments, depending on data type. Using compression in calculations is critical, as indexed data consumes less space than raw input after Splunk’s tokenization and compression processes.

Other options either misapply the compression ratio or the order of operations, producing incorrect totals.

References (Splunk Enterprise Documentation):

• Capacity Planning for Indexes – Storage Sizing and Compression Guidelines

• Managing Index Storage and Retention Policies

• Splunk Enterprise Admin Manual – Understanding Index Bucket Sizes

• Indexing Performance and Storage Optimization Guide

 The tailing_processor.log file would be the best place to search if you suspect there is a problem interpreting a regular expression in a monitor stanza. This log file contains information about how Splunk monitors files and directories, including any errors or warnings related to parsing the monitor stanza. The splunkd.log file contains general information about the Splunk daemon, but it may not have the specific details about the monitor stanza. The btool.log file contains information about the configuration files, but it does not log the runtime behavior of the monitor stanza. The metrics.log file contains information about the performance metrics of Splunk, but it does not log the event breaking issues. For more information, see About Splunk Enterprise logging in the Splunk documentation.

The correct configuration to meet the customer’s requirements is site_replication_factor = origin:1, site4:1, total:5. This means that each bucket will have one copy at the origin site, one copy at the disaster recovery site (site4), and three copies at any other sites. The total number of copies will be five, as required by the customer. The site_replication_factor determines how many copies of each bucket are stored across the sites in a multisite indexer cluster1. The site_search_factor determines how many copies of each bucket are searchable across the sites in a multisite indexer cluster2. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Configure the site replication factor 2: Configure the site search factor

According to the Splunk documentation1, the “Unknown sid” error message means that the search job associated with the link has expired or been deleted. The sid (search ID) is a unique identifier for each search job, and it is used to retrieve the results of the search. If the sid is not found, the search cannot be displayed. The other options are false because:

The users having insufficient permissions would result in a different error message, such as “You do not have permission to view this page” or "You do not have permission to run this search"1.

An add-on needing to be updated would not affect the validity of the sid, unless the add-on changes the search syntax or the data source in a way that makes the search invalid or inaccessible1.

One or more indexers being down would not cause the “Unknown sid” error, as the sid is stored on the search head, not the indexers. However, it could cause other errors, such as “Unable to distribute to peer” or "Search peer has the following message: not enough disk space"1.

 The index-time props.conf attributes that impact indexing performance are LINE_BREAKER and SHOULD_LINEMERGE. These attributes determine how Splunk breaks the incoming data into events and whether it merges multiple events into one. These operations can affect the indexing speed and the disk space consumption. The REPORT attribute does not impact indexing performance, as it is used to apply transforms at search time. The ANNOTATE_PUNCT attribute does not impact indexing performance, as it is used to add punctuation metadata to events at search time. For more information, see [About props.conf and transforms.conf] in the Splunk documentation.

According to the Splunk documentation1, increasing the current deployment client phone home interval can minimize performance issues by reducing the frequency of communication between the clients and the deployment server. This can also reduce the network traffic and the load on the deployment server. The other options are false because:

Modifying deploymentclient.conf to change from a Pull to Push mechanism is not possible, as Splunk does not support a Push mechanism for deployment server2.

Reducing the number of apps in the Manager Node repository will not affect the performance of the deployment server, as the apps are only downloaded when there is a change in the configuration or a new app is added3.

Decreasing the current deployment client phone home interval will increase the performance issues, as it will increase the frequency of communication between the clients and the deployment server, resulting in more network traffic and load on the deployment server1.

The correct answer is C. 100 GB per day. This is the expected minimum amount of storage required for data across an indexer cluster with the given input and parameters. The storage requirement can be calculated by adding the raw data size and the index files size, and then multiplying by the Replication Factor and the Search Factor1. In this case, the calculation is:

(15 GB + 35 GB) x 2 x 2 = 100 GB

The Replication Factor is the number of copies of each bucket that the cluster maintains across the set of peer nodes2. The Search Factor is the number of searchable copies of each bucket that the cluster maintains across the set of peer nodes3. Both factors affect the storage requirement, as they determine how many copies of the data are stored and searchable on the indexers. The other options are not correct, as they do not match the result of the calculation. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Estimate storage requirements 2: About indexer clusters and index replication 3: Configure the search factor

inputs.conf is a configuration file that contains settings for various types of data inputs, such as files, directories, network ports, scripts, and so on1.

initCrcLength is a setting that specifies the number of characters that the input uses to calculate the CRC (cyclic redundancy check) of a file1. The CRC is a value that uniquely identifies a file based on its content2.

crcSalt is another setting that adds a string to the CRC calculation to force the input to consume files that have matching CRCs1. This can be useful when files have identical headers or when files are renamed or rolled over2.

When troubleshooting a situation where some files within a directory are not being indexed, the ignored files are discovered to have long headers, the first thing that should be added to inputs.conf is to increase the value of initCrcLength. This is because by default, the input only performs CRC checks against the first 256 bytes of a file, which means that files with long headers may have matching CRCs and be skipped by the input2. By increasing the value of initCrcLength, the input can use more characters from the file to calculate the CRC, which can reduce the chances of CRC collisions and ensure that different files are indexed3.

Option C is the correct answer because it reflects the best practice for troubleshooting this situation. Option A is incorrect because decreasing the value of initCrcLength would make the CRC calculation less reliable and more prone to collisions. Option B is incorrect because adding a crcSalt with a static string would not help differentiate files with long headers, as they would still have matching CRCs. Option D is incorrect because adding a crcSalt with the attribute would add the full directory path to the CRC calculation, which would not help if the files are in the same directory2.

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s Search Head Clustering documentation explains that the cluster uses a majority-based election system. A captain is elected only when a node sees more than half of the cluster. In a two-site design where site1 has the majority of members, Splunk states that the majority site continues normal operation during a network partition. The minority site (site2) is not allowed to elect a captain and should not promote itself.

Splunk specifically warns administrators not to enable static captain on a minority site during a network split. Doing so creates two independent clusters, leading to configuration divergence and severe data-consistency issues. The documentation emphasizes that static captain should only be used for a complete loss of majority, not for a site partition.

Because Site1 maintains majority, it remains the active cluster and site2 does not perform any actions. Splunk states that minority-site members should simply wait until network communication is restored.

Thus the correct answer is B: No action is required.

The correct configuration attribute to set in server.conf on the cluster manager in a single-site indexer cluster is master_uri. This attribute specifies the URI of the cluster manager, which is required for the peer nodes and search heads to communicate with it1. The other attributes are not required for a single-site indexer cluster, but they are used for a multisite indexer cluster. The site attribute defines the site name for each node in a multisite indexer cluster2. The replication_factor attribute defines the number of copies of each bucket to maintain across the entire multisite indexer cluster3. The site_replication_factor attribute defines the number of copies of each bucket to maintain across each site in a multisite indexer cluster4. Therefore, option A is the correct answer, and options B, C, and D are incorrect.

1: Configure the cluster manager 2: Configure the site attribute 3: Configure the replication factor 4: Configure the site replication factor

 The parallelIngestionPipelines setting can be adjusted on the indexers and forwarders to improve Splunk performance. The parallelIngestionPipelines setting determines how many concurrent data pipelines are used to process the incoming data. Increasing the parallelIngestionPipelines setting can improve the data ingestion and indexing throughput, especially for high-volume data sources. The parallelIngestionPipelines setting can be adjusted on the indexers and forwarders by editing the limits.conf file. The parallelIngestionPipelines setting cannot be adjusted on the search head or the cluster master, because they are not involved in the data ingestion and indexing process.

Splunk Enterprise documentation clearly states that the best method to secure log traffic between Universal Forwarders (UFs) and Indexers is to implement Transport Layer Security (TLS) using signed SSL certificates. When Universal Forwarders send data to Indexers, this communication can be encrypted using SSL/TLS to prevent eavesdropping, data tampering, or interception while in transit.

Splunk provides default self-signed certificates out of the box, but these are only for testing or lab environments and should not be used in production. Production-grade security requires custom, signed SSL certificates — either from an internal Certificate Authority (CA) or a trusted public CA. These certificates validate both the sender (forwarder) and receiver (indexer), ensuring data integrity and authenticity.

In practice, this involves:

Generating or obtaining CA-signed certificates.

Configuring the forwarder’s outputs.conf to use SSL encryption (sslCertPath, sslPassword, and sslRootCAPath).

Configuring the indexer’s inputs.conf and server.conf to require and validate client certificates.

This configuration ensures end-to-end encryption for all log data transmitted from forwarders to indexers.

Routing traffic through a WAF (Option C) does not provide end-to-end encryption for Splunk’s internal communication, and securing search head–to–indexer communication (Option D) is unrelated to forwarder data flow.

References (Splunk Enterprise Documentation):

• Securing Splunk Enterprise: Encrypting Data in Transit Using SSL/TLS

• Configure Forwarder-to-Indexer Encryption

• Server and Forwarder Authentication with Signed Certificates

• Best Practices for Forwarder Management and Security Configuration

The splunk rebuild command is used for thawing the archive bucket. Thawing is the process of restoring frozen data back to Splunk for searching. Frozen data is data that has been archived or deleted from Splunk after reaching the end of its retention period. To thaw a bucket, the user needs to copy the bucket from the archive location to the thaweddb directory under SPLUNK_HOME/var/lib/splunk and run the splunk rebuild command to rebuild the .tsidx files for the bucket. The splunk collect command is used for collecting diagnostic data from a Splunk instance. The splunk convert command is used for converting configuration files from one format to another. The splunk dbinspect command is used for inspecting the status and properties of the buckets in an index.

 Changing the limits.conf value for max_searches_per_cpu to a higher value is the best option to increase scheduled search capacity on the search head cluster when a large number of searches are skipped across time. This value determines how many concurrent scheduled searches can run on each CPU core of the search head. Increasing this value will allow more scheduled searches to run at the same time, which will reduce the number of skipped searches. Creating a job server on the cluster, running the server.conf captain_is_adhoc_searchhead = true command, or adding another search head to the cluster are not the best options to increase scheduled search capacity on the search head cluster. For more information, see [Configure limits.conf] in the Splunk documentation.

The curl https://serverhost:8089/services/admin/inputstatus/TailingProcessor:FileStatus command is used to check the status of the tailed files when troubleshooting monitor inputs. Monitor inputs are inputs that monitor files or directories for new data and send the data to Splunk for indexing. The TailingProcessor:FileStatus endpoint returns information about the files that are being monitored by the Tailing Processor, such as the file name, path, size, position, and status. The splunk cmd btool inputs list | tail command is used to list the inputs configurations from the inputs.conf file and pipe the output to the tail command. The splunk cmd btool check inputs layer command is used to check the inputs configurations for syntax errors and layering. The curl https://serverhost:8089/services/admin/inputstatus/TailingProcessor:Tailstatus command does not exist, and it is not a valid endpoint.

The walklex command in Splunk is a specialized administrative search command used to translate and display LISPY (Splunk’s internal representation of search terms). LISPY is the logical search syntax Splunk uses to parse and execute search queries, and examining it helps administrators and developers debug search optimization, field extraction behavior, and index-time search efficiency.

When you run the command | walklex search="your_search_string", Splunk outputs how it tokenizes and interprets that query internally. This is particularly useful for understanding how Splunk’s search language maps to index-time fields and for diagnosing performance issues caused by inefficient search term parsing.

For example:

| walklex search="error OR failure host=server01"

Displays the corresponding LISPY translation used by Splunk’s search subsystem.

Other options are unrelated:

dbinspect provides index bucket metadata.

Monitoring Console shows performance metrics and health status.

Search Job Inspector analyzes search execution phases but doesn’t expose LISPY.

Thus, the correct and Splunk-documented tool for LISPY inspection is the walklex command.

References (Splunk Enterprise Documentation):

• walklex Command Reference – LISPY and Search Debugging

• Understanding Search Language Parsing in Splunk

• Search Internals: How Splunk Interprets Queries

• Splunk Search Performance Troubleshooting Tools

To resync a stale KV Store member in a search head cluster, you need to stop the search head that has the stale KV Store member, run the command splunk clean kvstore --local, and then restart the search head. This triggers the initial synchronization from other KV Store members12.

The command splunk resync kvstore [-source sourceId] is used to resync the entire KV Store cluster from one of the members, not a single member. This command can only be invoked from the node that is operating as search head cluster captain2.

The command splunk clean eventdata -local is used to delete all indexed data from a standalone indexer or a cluster peer node, not to resync the KV Store3.

KV Store is a feature of Splunk Enterprise that allows apps to store and retrieve data within the context of an app1.

KV Store resides on search heads and replicates data across the members of a search head cluster1.

KV Store resiliency refers to the ability of KV Store to maintain data availability and consistency in the event of failures or disruptions2.

One of the factors that affects KV Store resiliency is the network latency between search heads, which can impact the speed and reliability of data replication2.

Decreasing latency between search heads can improve KV Store resiliency by reducing the chances of data loss, inconsistency, or corruption2.

The other options are not directly related to KV Store resiliency. Faster storage, indexer CPU and memory, and Operations Log size may affect other aspects of Splunk performance, but not KV Store345.

Splunk Enterprise allows administrators to dynamically increase logging verbosity for a specific search by adding a | noop log_debug=* command immediately after the base search. This method provides temporary, search-specific debug logging without requiring global configuration changes or restarts.

The noop (no operation) command passes all results through unchanged but can trigger internal logging actions. When paired with the log_debug=* argument, it instructs Splunk to record detailed debug-level log messages for that specific search execution in search.log and the relevant internal logs.

This approach is officially documented for troubleshooting complex search issues such as:

Unexpected search behavior or slow performance.

Field extraction or command evaluation errors.

Debugging custom search commands or macros.

Using this method is safer and more efficient than modifying server-wide logging configurations (server.conf or limits.conf), which can affect all users and increase log noise. The “Server logging” page in Splunk Web (Option D) adjusts global logging levels, not per-search debugging.

References (Splunk Enterprise Documentation):

• Search Debugging Techniques and the noop Command

• Understanding search.log and Per-Search Logging Control

• Splunk Search Job Inspector and Debugging Workflow

• Troubleshooting SPL Performance and Field Extraction Issues

 The algorithm used to determine captaincy in a Splunk search head cluster is Raft distributed consensus. Raft is a consensus algorithm that is used to elect a leader among a group of nodes in a distributed system. In a Splunk search head cluster, Raft is used to elect a captain among the cluster members. The captain is the cluster member that is responsible for coordinating the search activities, replicating the configurations and apps, and pushing the knowledge bundles to the search peers. The captain is dynamically elected based on various criteria, such as CPU load, network latency, and search load. The captain can change over time, depending on the availability and performance of the cluster members. Rapt, Rift, and Round-robin are not valid algorithms for determining captaincy in a Splunk search head cluster

Splunk Enterprise licensing is based on the amount of data that is ingested and indexed by the Splunk platform per day1. The data that counts against the license is the data that is stored in the indexes that are visible to the users and searchable by the Splunk software2. The indexes that are visible and searchable by default are the main index and any custom indexes that are created by the users or the apps3. The main index is the default index where Splunk Enterprise stores all data, unless otherwise specified4.

Option B is the correct answer because the data for the main index will count against the ingest-based license, as it is a visible and searchable index by default. Option A is incorrect because the summary index is a special type of index that stores the results of scheduled reports or accelerated data models, which do not count against the license. Option C is incorrect because the _metrics index is an internal index that stores metrics data about the Splunk platform performance, which does not count against the license. Option D is incorrect because the _introspection index is another internal index that stores data about the impact of the Splunk software on the host system, such as CPU, memory, disk, and network usage, which does not count against the license.

According to the indexes.conf reference in Splunk Enterprise, the parameter maxDataSize controls the maximum size (in GB or MB) of a single hot bucket before Splunk rolls it to a warm bucket. When the value is set to auto_high_volume on a 64-bit system, Splunk automatically sets the maximum hot bucket size to 10 GB.

The “auto” settings allow Splunk to choose optimized values based on the system architecture:

auto: Default hot bucket size of 750 MB (32-bit) or 10 GB (64-bit).

auto_high_volume: Specifically tuned for high-ingest indexes; on 64-bit systems, this equals 10 GB per hot bucket.

auto_low_volume: Uses smaller bucket sizes for lightweight indexes.

The purpose of larger hot bucket sizes on 64-bit systems is to improve indexing performance and reduce the overhead of frequent bucket rolling during heavy data ingestion. The documentation explicitly warns that these sizes differ on 32-bit systems due to memory addressing limitations.

Thus, for high-throughput environments running 64-bit operating systems, auto_high_volume = 10 GB is the correct and Splunk-documented configuration.

References (Splunk Enterprise Documentation):

• indexes.conf – maxDataSize Attribute Reference

• Managing Index Buckets and Data Retention

• Splunk Enterprise Admin Manual – Indexer Storage Configuration

• Splunk Performance Tuning: Bucket Management and Hot/Warm Transitions