Splunk SPLK-2002 Splunk Enterprise Certified Architect Exam Practice Test

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

According to the Splunk documentation1, in a search head cluster, the KV Store Primary is the same node as the search head cluster captain. The KV Store Primary is responsible for coordinating the replication of KV Store data across the cluster members. When any node receives a write request, the KV Store delegates the write to the KV Store Primary. The KV Store keeps the reads local, however. This ensures that the KV Store data is consistent and available across the cluster.

According to the Splunk blog1, the Universal Forwarder is ideal for collecting data from high-velocity data sources, such as a syslog server, due to its smaller footprint and faster performance. The Universal Forwarder performs minimal processing and sends raw or unparsed data to the indexers, reducing the network traffic and the load on the forwarders. The other options are false because:

When most of the data requires masking, a Heavy Forwarder is needed, as it can perform advanced filtering and data transformation before forwarding the data2.

When data comes directly from a database server, a Heavy Forwarder is needed, as it can run modular inputs such as DB Connect to collect data from various databases2.

When a modular input is needed, a Heavy Forwarder is needed, as the Universal Forwarder does not include a bundled version of Python, which is required for most modular inputs2.

According to the Splunk documentation1, the _indextime field is the time when Splunk indexed the event. This field can be used to confirm duplicate event issues from failed file monitoring, as it can show you when each duplicate event was indexed and if they have different _indextime values. You can use the Search Job Inspector to inspect the search job that returns the duplicate events and check the _indextime field for each event2. The other options are false because:

The _time field is the time extracted from the event data, not the time when Splunk indexed the event. This field may not reflect the actual indexing time, especially if the event data has a different time zone or format than the Splunk server1.

The _index_latest field is not a valid Splunk internal field, as it does not exist in the Splunk documentation or the Splunk data model3.

The latest field is a field that represents the latest time bound of a search, not the time when Splunk indexed the event. This field is used to specify the time range of a search, along with the earliest field4.

The additional information that is needed to calculate the daily disk consumption, per indexer, if indexer clustering is implemented, is the total daily indexing volume, the number of peer nodes, the replication factor, and the search factor. These information are required to estimate how much data is ingested, how many copies of raw data and searchable data are maintained, and how many indexers are involved in the cluster. The number of accelerated searches, the number of search heads, and the total disk size across the cluster are not relevant for calculating the daily disk consumption, per indexer. For more information, see [Estimate your storage requirements] in the Splunk documentation.

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 client filters available in serverclass.conf are DNS name, IP address, and platform (machine type). These filters allow the administrator to specify which forwarders belong to a server class and receive the apps and configurations from the deployment server. The Splunk server role is not a valid client filter in serverclass.conf, as it is not a property of the forwarder. For more information, see [Use forwarder management filters] in the Splunk documentation.

This is the fall-back method for Splunk if .delta replication fails during knowledge bundle replication. Knowledge bundle replication is the process of distributing the knowledge objects, such as lookups, macros, and field extractions, from the search head cluster to the indexer cluster1. Splunk uses two methods of knowledge bundle replication: .delta replication and .bundle replication1. .Delta replication is the default and preferred method, as it only replicates the changes or updates to the knowledge objects, which reduces the network traffic and disk space usage1. However, if .delta replication fails for some reason, such as corrupted files or network errors, Splunk automatically switches to .bundle replication, which replicates the entire knowledge bundle, regardless of the changes or updates1. This ensures that the knowledge objects are always synchronized between the search head cluster and the indexer cluster, but it also consumes more network bandwidth and disk space1. The other options are not valid fall-back methods for Splunk. Option A, restarting splunkd, is not a method of knowledge bundle replication, but a way to restart the Splunk daemon on a node2. This may or may not fix the .delta replication failure, but it does not guarantee the synchronization of the knowledge objects. Option B, .delta replication, is not a fall-back method, but the primary method of knowledge bundle replication, which is assumed to have failed in the question1. Option D, restarting mongod, is not a method of knowledge bundle replication, but a way to restart the MongoDB daemon on a node3. This is not related to the knowledge bundle replication, but to the KV store replication, which is a different process3. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: How knowledge bundle replication works 2: Start and stop Splunk Enterprise 3: Restart the KV store

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.

The following may explain the problem of why a colleague cannot see the src_ip field in their search results: The field was extracted as a private knowledge object, and the colleague did not explicitly use the field in the search and the search was set to Fast Mode. A knowledge object is a Splunk entity that applies some knowledge or intelligence to the data, such as a field extraction, a lookup, or a macro. A knowledge object can have different permissions, such as private, app, or global. A private knowledge object is only visible to the user who created it, and it cannot be shared with other users. A field extraction is a type of knowledge object that extracts fields from the raw data at index time or search time. If a field extraction is created as a private knowledge object, then only the user who created it can see the extracted field in their search results. A search mode is a setting that determines how Splunk processes and displays the search results, such as Fast, Smart, or Verbose. Fast mode is the fastest and most efficient search mode, but it also limits the number of fields and events that are displayed. Fast mode only shows the default fields, such as _time, host, source, sourcetype, and _raw, and any fields that are explicitly used in the search. If a field is not used in the search and it is not a default field, then it will not be shown in Fast mode. The events are tagged as communicate, but are missing the network tag, and the Typing Queue, which does regular expression replacements, is blocked, are not valid explanations for the problem. Tags are labels that can be applied to fields or field values to make them easier to search. Tags do not affect the visibility of fields, unless they are used as filters in the search. The Typing Queue is a component of the Splunk data pipeline that performs regular expression replacements on the data, such as replacing IP addresses with host names. The Typing Queue does not affect the field extraction process, unless it is configured to do so

All of the forwarders would still be phoning home to the old deployment server, because the forwarders are configured to use the old deployment server in $SPLUNK_HOME/etc/system/local. This is the local configuration directory that contains the settings that override the default settings in $SPLUNK_HOME/etc/system/default. The deploymentclient.conf file in the local directory specifies the targetUri of the deployment server that the forwarder contacts for configuration updates and apps. If the forwarders have the old deployment server’s targetUri in the local directory, they will ignore the updated deploymentclient.conf file that was deployed by the old deployment server, because the local settings have higher precedence than the deployed settings. To fix this issue, the forwarders should either remove the deploymentclient.conf file from the local directory, or update it with the new deployment server’s targetUri. Option C is the correct answer. Option A is incorrect because a version mismatch between the forwarders and the new deployment server would not prevent the forwarders from phoning home to the new deployment server, as long as they are compatible versions. Option B is incorrect because the new deployment server is configured and running, and there is no indication that it is not accepting connections from the forwarders. Option D is incorrect because the pass4SymmKey is the shared secret key that the deployment server and the forwarders use to authenticate each other. It does not affect the forwarders’ ability to phone home to the new deployment server, as long as it is the same on both sides12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Configuredeploymentclients 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Wheretofindtheconfigurationfiles

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.

The correct answer is C. Knowledge Object replication. This is a valid use case that a search head cluster addresses, as it ensures that all the search heads in the cluster have the same set of knowledge objects, such as saved searches, dashboards, reports, and alerts1. The search head cluster replicates the knowledge objects across the cluster members, and synchronizes any changes or updates1. This provides a consistent user experience and avoids data inconsistency or duplication1. The other options are not valid use cases that a search head cluster addresses. Option A, providing redundancy in the event a search peer fails, is not a use case for a search head cluster, but for an indexer cluster, which maintains multiple copies of the indexed data and can recover from indexer failures2. Option B, search affinity, is not a use case for a search head cluster, but for a multisite indexer cluster, which allows the search heads to preferentially search the data on the local site, rather than on a remote site3. Option D, increased Search Factor (SF), is not a use case for a search head cluster, but for an indexer cluster, which determines how many searchable copies of each bucket are maintained across the indexers4. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: About search head clusters 2: About indexer clusters and index replication 3: Configure search affinity 4: Configure the search factor

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.”

Multiple search pipelines should be enabled only if CPU and memory resources are significantly under-utilized. Search pipelines are the processes that execute search commands and return results. Multiple search pipelines can improve the search performance by running concurrent searches in parallel. However, multiple search pipelines also consume more CPU and memory resources, which can affect the overall system performance. Therefore, multiple search pipelines should be enabled only if there are enough CPU and memory resources available, and if the system is not bottlenecked by disk I/O or network bandwidth. The number of concurrent users, the disk IOPS, and the Splunk Enterprise version are not relevant factors for enabling multiple search pipelines

Adding more search peers and making sure forwarders distribute data evenly across all indexers will provide the most search performance improvement when the distributed deployment is approaching its capacity. Adding more search peers will increase the search concurrency and reduce the load on each indexer. Distributing data evenly across all indexers will ensure that the search workload is balanced and no indexer becomes a bottleneck. Replacing the indexer storage to SSD will improve the search performance, but it is a costly and time-consuming option. Adding more search heads will not improve the search performance if the indexers are the bottleneck. Rescheduling slow searches to run during an off-peak time will reduce the search contention, but it will not improve the search performance for each individual search. For more information, see [Scale your indexer cluster] and [Distribute data across your indexers] in the Splunk documentation.

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

 When planning a search head cluster, the following statement is true: All indexers must belong to the underlying indexer cluster (no standalone indexers). A search head cluster is a group of search heads that share configurations, apps, and search jobs. A search head cluster requires an indexer cluster as its data source, meaning that all indexers that provide data to the search head cluster must be members of the same indexer cluster. Standalone indexers, or indexers that are not part of an indexer cluster, cannot be used as data sources for a search head cluster. All search heads do not have to use the same operating system, as long as they are compatible with the Splunk version and the indexer cluster. All search heads do not have to be members of the cluster, as standalone search heads can also search the indexer cluster, but they will not have the benefits of configuration replication and load balancing. The search head captain does not have to be assigned to the largest search head in the cluster, as the captain is dynamically elected from among the cluster members based on various criteria, such as CPU load, network latency, and search load.

Splunk Enterprise performs a CRC check against the first and last 256 bytes of a file by default, as stated in the inputs.conf specification. This is controlled by the initCrcLength parameter, which can be changed if needed. The CRC check helps Splunk Enterprise to avoid re-indexing the same file twice, even if it is renamed or rotated, as long as the content does not change. However, this also means that Splunk Enterprise might miss some files that have the same CRC but different content, especially if they have identical headers. To avoid this, the crcSalt parameter can be used to add some extra information to the CRC calculation, such as the full file path or a custom string. This ensures that each file has a unique CRC and is indexed by Splunk Enterprise. You can read more about crcSalt and initCrcLength in the How log file rotation is handled documentation.

According to Splunk Enterprise Search Performance and Deployment Optimization guidelines, the phone home interval (configured for deployment clients communicating with a Deployment Server) has no impact on search performance.

The phone home mechanism controls how often deployment clients check in with the Deployment Server for configuration updates or new app bundles. This process occurs independently of the search subsystem and does not consume indexer or search head resources that affect query speed, indexing throughput, or search concurrency.

In contrast:

Increasing the number of indexers (Option B) improves search performance by distributing indexing and search workloads across more nodes.

Workload Management (Option C) allows admins to prioritize compute and memory resources for critical searches, optimizing performance under load.

Increasing search heads (Option D) can enhance concurrency and user responsiveness by distributing search scheduling and ad-hoc query workloads.

Therefore, adjusting the phone home interval is strictly an administrative operation and has no measurable effect on Splunk search or indexing performance.

References (Splunk Enterprise Documentation):

• Deployment Server: Managing Phone Home Intervals

• Search Performance Optimization and Resource Management

• Distributed Search Architecture and Scaling Best Practices

• Workload Management Overview – Resource Allocation in Search Operations

A search head cluster is a group of Splunk Enterprise search heads that share configurations, job scheduling, and search artifacts1. The deployer is a Splunk Enterprise instance that distributes apps and other configurations to the cluster members1. The local folder of each Splunk app contains the custom configurations that override the default settings2. The default folder of each Splunk app contains the default configurations that are provided by the app2.

By default, when the deployer pushes an app to the search head cluster, it merges the local folder of the app into the default folder and deploys the merged folder to the search heads3. This means that the custom configurations in the local folder will take precedence over the default settings in the default folder. However, this also means that the local folder of the app on the search heads will be empty, unless the app is modified through the search head UI3.

Option B is the correct answer because it reflects the default behavior of the deployer when pushing apps to the search head cluster. Option A is incorrect because the local folder is not copied to the local folder on the search heads, but merged into the default folder. Option C is incorrect because all the .conf files in the local folder are deployed to the search heads, not only certain ones. Option D is incorrect because the local folder is not ignored, but merged into the default folder.

 A multi-site indexer cluster can be configured by directly editing SPLUNK_HOME/etc/system/local/server.conf or running a splunk edit cluster-config command from the CLI. These methods allow the administrator to specify the site attribute for each indexer node and the site_replication_factor and site_search_factor for the cluster. Configuring a multi-site indexer cluster via Splunk Web or directly editing SPLUNK_HOME/etc/system/default/server.conf are not supported methods. For more information, see Configure the indexer cluster with server.conf in the Splunk documentation.

As per Splunk Enterprise Search Performance documentation, the most significant factor affecting search performance when querying across a large number of buckets is disk I/O throughput. A search that spans “All Time” forces Splunk to inspect all historical buckets (hot, warm, cold, and potentially frozen if thawed), even if only a few events match the query. This dramatically increases the amount of data read from disk, making the search bound by I/O performance rather than CPU or memory.

Increasing the number of indexing pipelines (Option B) only benefits data ingestion, not search performance. Changing to a real-time search (Option D) does not help because real-time searches are optimized for streaming new data, not historical queries. The indexed_realtime_use_by_default setting (Option C) applies only to streaming indexed real-time searches, not historical “All Time” searches.

To improve performance for such searches, Splunk documentation recommends enhancing disk I/O capability — typically through SSD storage, increased disk bandwidth, or optimized storage tiers. Additionally, creating summary indexes or accelerated data models may help for repeated “All Time” queries, but the most direct improvement comes from faster disk performance since Splunk must scan large numbers of buckets for even small result sets.

References (Splunk Enterprise Documentation):

• Search Performance Tuning and Optimization

• Understanding Bucket Search Mechanics and Disk I/O Impact

• limits.conf Parameters for Search Performance

• Storage and Hardware Sizing Guidelines for Indexers and Search Heads

Data retention is optional in the data source assessment because it is not directly related to the ingestion process. Data retention is determined by the index configuration and the storage capacity of the Splunk platform. Data format, data location, and data volume are all essential information for planning how to collect, parse, and index the data source.

 ITSI can impact the planning of a Splunk deployment depending on the Key Performance Indicators (KPIs) that are being tracked. KPIs are metrics that measure the health and performance of IT services and business processes. ITSI collects, analyzes, and displays KPI data from various data sources in Splunk. Depending on the number, frequency, and complexity of the KPIs, additional infrastructure may be needed to support the data ingestion, processing, and visualization. ITSI does not require a dedicated deployment server, nor does it affect the number of users using ITSI. ITSI in a Splunk deployment does require additional hardware resources, such as CPU, memory, and disk space, to run the ITSI components and apps

Searching for closed_txn=0 in this search filters results to situations where Splunk was started, but not stopped. This means that the transaction was not completed, and Splunk crashed before it could finish the pipelines. The closed_txn field is added by the transaction command, and it indicates whether the transaction was closed by an event that matches the endswith condition1. A value of 0 means that the transaction was not closed, and a value of 1 means that the transaction was closed1. Therefore, option D is the correct answer, and options A, B, and C are incorrect.

1: transaction command overview

Index files (. tsidx files) are the main components of an index that store the raw data and the inverted index of terms. They take the most space in an index, especially if the raw data has many unique terms that increase the size of the inverted index. Bloom filters, source metadata, and sourcetype metadata are much smaller in comparison and do not depend on the number of unique terms in the raw data.

 The following statements are true about Splunk indexer clustering:

All peer nodes must run exactly the same Splunk version. This is a requirement for indexer clustering, as different Splunk versions may have different data formats or features that are incompatible with each other. All peer nodes must run the same Splunk version as the master node and the search heads that connect to the cluster.

The search head must run the same or a later Splunk version than the peer nodes. This is a recommendation for indexer clustering, as a newer Splunk version may have new features or bug fixes that improve the search functionality or performance. The search head should not run an older Splunk version than the peer nodes, as this may cause search errors or failures. The following statements are false about Splunk indexer clustering:

The master node must run the same or a later Splunk version than the search heads. This is not a requirement or a recommendation for indexer clustering, as the master node does not participate in the search process. The master node should run the same Splunk version as the peer nodes, as this ensures the cluster compatibility and functionality.

The peer nodes must run the same or a later Splunk version than the master node. This is not a requirement or a recommendation for indexer clustering, as the peer nodes do not coordinate the cluster activities. The peer nodes should run the same Splunk version as the master node, as this ensures the cluster compatibility and functionality. For more information, see [About indexer clusters and index replication] and [Upgrade an indexer cluster] in the Splunk documentation.

The correct answer is A. Change frozenTimePeriodInSecs to a larger value. This is a possible solution to reduce the need to thaw buckets, as it increases the time period before a bucket is frozen and removed from the index1. The frozenTimePeriodInSecs attribute specifies the maximum age, in seconds, of the data that the index can contain1. By setting it to a larger value, the Splunk administrator can keep the data in the index for a longer time, and avoid having to thaw the buckets frequently. The other options are not effective solutions to reduce the need to thaw buckets. Option B, changing maxTotalDataSizeMB to a smaller value, would actually increase the need to thaw buckets, as it decreases the maximum size, in megabytes, of an index2. This means that the index would reach its size limit faster, and more buckets would be frozen and removed. Option C, changing maxHotSpanSecs to a larger value, would not affect the need to thaw buckets, as it only changes the maximum lifetime, in seconds, of a hot bucket3. This means that the hot bucket would stay hot for a longer time, but it would not prevent the bucket from being frozen eventually. Option D, changing coldToFrozenDir to a different location, would not reduce the need to thaw buckets, as it only changes the destination directory for the frozen buckets4. This means that the buckets would still be frozen and removed from the index, but they would be stored in a different location. Therefore, option A is the correct answer, and options B, C, and D are incorrect.

1: Set a retirement and archiving policy 2: Configure index size 3: Bucket rotation and retention 4: Archive indexed data

According to the Splunk Search Head Clustering Troubleshooting Guide, when a Search Head Cluster (SHC) member is reverted from a backup or experiences configuration drift (e.g., an outdated Raft state), it can fail to rejoin the cluster due to inconsistent Raft metadata. The Raft database stores the SHC’s internal consensus and replication state, including knowledge object synchronization, captain election history, and peer membership information.

If this Raft metadata becomes corrupted or outdated (as in the scenario where a node is restored from backup), the recommended and Splunk-supported remediation is to clean the Raft metadata using:

splunk clean raft

This command resets the node’s local Raft state so it can re-synchronize with the current SHC captain and rejoin the cluster cleanly.

The steps generally are:

Stop the affected SHC member.

Run splunk clean raft on that node.

Restart Splunk.

Verify that it successfully rejoins the SHC.

Deleting configuration stanzas or forcing re-addition (Options B and C) can lead to further inconsistency or data loss. Reviewing logs (Option A) helps diagnose issues but does not resolve Raft corruption.

References (Splunk Enterprise Documentation):

• Troubleshooting Raft Metadata Corruption in Search Head Clusters

• splunk clean raft Command Reference

• Search Head Clustering: Recovering from Backup and Membership Failures

• Splunk Enterprise Admin Manual – Raft Consensus and SHC Maintenance

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

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.

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 Splunk’s Deployment Planning and Implementation Guidelines, one of the most critical elements of a Splunk deployment plan is a comprehensive data source inventory and current logging details. This information defines the scope of data ingestion and directly influences sizing, architecture design, and licensing.

A proper deployment plan should identify:

All data sources (such as syslogs, application logs, network devices, OS logs, databases, etc.)

Expected daily ingest volume per source

Log formats and sourcetypes

Retention requirements and compliance constraints

This data forms the foundation for index sizing, forwarder configuration, and storage planning. Without a well-defined data inventory, Splunk architects cannot accurately determine hardware capacity, indexing load, or network throughput requirements.

While stakeholder mapping, topology diagrams, and continuity plans (Options A, B, D) are valuable in a broader IT project, Splunk’s official guidance emphasizes logging details and source inventory as mandatory for a deployment plan. It ensures that the Splunk environment is properly sized, licensed, and aligned with business data visibility goals.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Deployment Planning Manual – Data Source Inventory Requirements

• Capacity Planning for Indexer and Search Head Sizing

• Planning Data Onboarding and Ingestion Strategies

• Splunk Architecture and Implementation Best Practices

 To reduce the captain’s work load in a search head cluster, the setting that will prevent scheduled searches from running on the captain is captain_is_adhoc_searchhead = true (on the current captain). This setting will designate the current captain as an ad hoc search head, which means that it will not run any scheduled searches, but only ad hoc searches initiated by users. This will reduce the captain’s work load and improve the search head cluster performance. The adhoc_searchhead = true (on all members) setting will designate all search head cluster members as ad hoc search heads, which means that none of them will run any scheduled searches, which is not desirable. The adhoc_searchhead = true (on the current captain) setting will have no effect, as this setting is ignored by the captain. The captain_is_adhoc_searchhead = true (on all members) setting will have no effect, as this setting is only applied to the current captain. For more information, see Configure the captain as an ad hoc search head in the Splunk documentation.

Splunk Enterprise uses the _audit index to log and store all user activity and audit-related information. This includes details such as user logins, searches executed, configuration changes, role modifications, and app management actions.

The _audit index is populated by data collected from the Splunkd audit logger and records actions performed through both Splunk Web and the CLI. Each event in this index typically includes fields like user, action, info, search_id, and timestamp, allowing administrators to track activity across all Splunk users and components for security, compliance, and accountability purposes.

The _internal index, by contrast, contains operational logs such as metrics.log and scheduler.log used for system performance and health monitoring. _kvstore stores internal KV Store metadata, and _telemetry is used for optional usage data reporting to Splunk.

The _audit index is thus the authoritative source for user behavior monitoring within Splunk environments and is a key component of compliance and security auditing.

References (Splunk Enterprise Documentation):

• Audit Logs and the _audit Index – Monitoring User Activity

• Splunk Enterprise Security and Compliance: Tracking User Actions

• Splunk Admin Manual – Overview of Internal Indexes (_internal, _audit, _introspection)

• Splunk Audit Logging and User Access Monitoring

A single-site indexer cluster is a group of Splunk Enterprise instances that index and replicate data across the cluster1. A bucket is a directory that contains indexed data, along with metadata and other information2. A replication factor is the number of copies of each bucket that the cluster maintains1. A search factor is the number of searchable copies of each bucket that the cluster maintains1. A searchable copy is a copy that contains both the raw data and the index files3. A search head is a Splunk Enterprise instance that coordinates the search activities across the peer nodes1.

Option D is the correct answer because it reflects the definitions of replication factor and search factor. The cluster will ensure that there are at least three copies of each bucket, one on each peer node, to satisfy the replication factor of 3. The cluster will also ensure that there are at least two searchable copies of each bucket, one primary and one searchable, to satisfy the search factor of 2. The primary copy is the one that the search head uses to run searches, and the searchable copy is the one that can be promoted to primary if the original primary copy becomes unavailable3.

Option A is incorrect because it confuses the replication factor and the search factor. The cluster will ensure there are at least three copies of each bucket, not two, to meet the replication factor of 3. The cluster will ensure there are at least two copies of searchable metadata, not three, to meet the search factor of 2.

Option B is incorrect because it uses the wrong terms. The cluster will ensure there are at least, not at most, three copies of each bucket, to meet the replication factor of 3. The cluster will ensure there are at least, not at most, two copies of searchable metadata, to meet the search factor of 2.

Option C is incorrect because it has nothing to do with the replication factor or the search factor. The cluster does not limit the number of search heads that can access the bucket at the same time. The search head can search across multiple clusters, and the cluster can serve multiple search heads1.

1: The basics of indexer cluster architecture - Splunk Documentation 2: About buckets - Splunk Documentation 3: Search factor - Splunk Documentation

According to Splunk Enterprise documentation for the Monitoring Console (MC), the data displayed in its dashboards is sourced primarily from two internal mechanisms — REST API calls and metrics.log.

The Monitoring Console (formerly known as the Distributed Management Console, or DMC) uses REST API endpoints to collect system-level information from all connected instances, such as indexer clustering status, license usage, and search head performance. These REST calls pull real-time configuration and performance data from Splunk’s internal management layer (/services/server/status, /services/licenser, /services/cluster/peers, etc.).

Additionally, the metrics.log file is one of the main data sources used by the Monitoring Console. This log records Splunk’s internal performance metrics, including pipeline latency, queue sizes, indexing throughput, CPU usage, and memory statistics. Dashboards like “Indexer Performance,” “Search Performance,” and “Resource Usage” are powered by searches over the _internal index that reference this log.

Other tools listed — such as btool (configuration troubleshooting utility) and diag (diagnostic archive generator) — are not used as runtime data sources for Monitoring Console dashboards. They assist in troubleshooting but are not actively queried by the MC.

References (Splunk Enterprise Documentation):

• Monitoring Console Overview – Data Sources and Architecture

• metrics.log Reference – Internal Performance Data Collection

• REST API Usage in Monitoring Console

• Distributed Management Console Configuration Guide

 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

According to the Splunk documentation1, the Splunk deployment topology is the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters. The deployment topology defines the number and type of Splunk components, such as forwarders, indexers, search heads, and deployers, that you need to install and configure for your distributed deployment. The deployment topology also determines the network and hardware requirements, the data flow and replication, the high availability and disaster recovery options, and the security and performance considerations for your deployment2. The other options are false because:

Data source inventory is not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as it is a preliminary step that helps you identify the types, formats, locations, and volumes of data that you want to collect and analyze with Splunk. Data source inventory is important for planning your data ingestion and retention strategies, but it does not directly affect the installation and configuration of Splunk components3.

Data policy definitions are not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as they are the rules and guidelines that govern how you handle, store, and protect your data. Data policy definitions are important for ensuring data quality, security, and compliance, but they do not directly affect the installation and configuration of Splunk components4.

Education and training plans are not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as they are the learning resources and programs that help you and your team acquire the skills and knowledge to use Splunk effectively. Education and training plans are important for enhancing your Splunk proficiency and productivity, but they do not directly affect the installation and configuration of Splunk components5.

Primary rebalancing automatically occurs when a rolling restart completes, a master node rejoins the cluster, or a peer node joins or rejoins the cluster. These events can cause the distribution of primary buckets to become unbalanced, so the master node will initiate a rebalancing process to ensure that each peer node has roughly the same number of primary buckets. Primary rebalancing does not occur when a captain joins or rejoins the cluster, because the captain is a search head cluster component, not an indexer cluster component. The captain is responsible for search head clustering, not indexer clustering

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 correct action to take first if a deployment client is not updating apps is to choose a corrective action based on the splunkd.log of the deployment client. This log file contains information about the communication between the deployment server and the deployment client, and it can help identify the root cause of the problem1. The other actions may or may not help, depending on the situation, but they are not the first steps to take. Choosing a longer phone home interval may reduce the load on the deployment server, but it will also delay the updates for the deployment clients2. Increasing the number of CPU cores or the amount of memory for the deployment server may improve its performance, but it will not fix the issue if the problem is on the deployment client side3. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Troubleshoot deployment server issues 2: Configure deployment clients 3: Hardware and software requirements for the deployment server

 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.

 The Splunk Universal Forwarder is the only Splunk Enterprise offering that has its own license. The Splunk Universal Forwarder license allows the forwarder to send data to any Splunk Enterprise or Splunk Cloud instance without consuming any license quota. The Splunk Heavy Forwarder does not have its own license, but rather consumes the license quota of the Splunk Enterprise or Splunk Cloud instance that it sends data to. The Splunk Cloud Forwarder and the Splunk Forwarder Management are not separate Splunk Enterprise offerings, but rather features of the Splunk Cloud service. For more information, see [About forwarder licensing] in the Splunk documentation.

The correct Splunk deployment to have the recommended minimum components for a high-availability search head cluster is 3 search heads, 1 deployer, 3 indexers. This configuration ensures that the search head cluster has at least three members, which is the minimum number required for a quorum and failover1. The deployer is a separate instance that manages the configuration updates for the search head cluster2. The indexers are the nodes that store and index the data, and having at least three of them provides redundancy and load balancing3. The other options are not recommended, as they either have less than three search heads or less than three indexers, which reduces the availability and reliability of the cluster. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: About search head clusters 2: Use the deployer to distribute apps and configuration updates 3: About indexer clusters and index replication

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

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

 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.

When Splunk indexes data in a non-clustered environment, it creates index and .tsidx files by default. The index files contain the raw data that Splunk has ingested, compressed and encrypted. The .tsidx files contain the time-series index that maps the timestamps and event IDs of the raw data. The rawdata and index files are not the correct terms for the files that Splunk creates. The compressed and .tsidx files are partially correct, but compressed is not the proper name for the index files. The compressed and meta data files are also partially correct, but meta data is not the proper name for the .tsidx files.

The Splunk Enterprise Security and Encryption documentation specifies that the primary mechanism for securing data in motion within a Splunk environment is to enable TLS/SSL encryption between forwarders and indexers. This ensures that log data transmitted from Universal Forwarders or Heavy Forwarders to Indexers is fully encrypted and protected from interception or tampering.

The correct configuration involves setting up signed SSL certificates on both forwarders and indexers:

On the forwarder, TLS settings are defined in outputs.conf, specifying parameters like sslCertPath, sslPassword, and sslRootCAPath.

On the indexer, TLS is enabled in inputs.conf and server.conf using the same shared CA for validation.

Splunk’s documentation explicitly states that this configuration protects data-in-transit between the collection (forwarder) and indexing (storage) tiers — which is the critical link where sensitive log data is most vulnerable.

Other communication channels (e.g., deployment server to clients or browser to Splunk Web) can also use encryption but do not secure the ingestion pipeline that handles the indexed data stream. Therefore, TLS should be implemented between Splunk forwarders and indexers.

References (Splunk Enterprise Documentation):

• Securing Data in Transit with SSL/TLS

• Configure Forwarder-to-Indexer Encryption Using SSL Certificates

• Server and Forwarder Authentication Setup Guide

• Splunk Enterprise Admin Manual – Security and Encryption Best Practices

A deployment plan should include business continuity and disaster recovery plans, current logging details and data source inventory, and current and future topology diagrams of the IT environment. These elements are essential for planning, designing, and implementing a Splunk deployment that meets the business and technical requirements. A comprehensive list of stakeholders, either direct or indirect, is not part of the deployment plan, but rather part of the project charter. For more information, see Deployment planning in the Splunk documentation.

The repFactor (replication factor) attribute in the indexes.conf file determines whether an index participates in indexer clustering and how many copies of its data are replicated across peer nodes.

When repFactor is set to 0, it explicitly instructs Splunk to exclude that index from participating in the cluster replication and management process. This means:

The index is not replicated across peer nodes.

It will not be managed by the Cluster Manager.

It exists only locally on the indexer where it was created.

Such indexes are typically used for local-only storage, such as _internal, _audit, or other custom indexes that store diagnostic or node-specific data.

By contrast:

repFactor=auto allows the index to inherit the cluster-wide replication policy from the Cluster Manager.

available_sites and site_mappings relate to multisite configurations, controlling where copies of the data are stored, but they do not remove the index from clustering.

Setting repFactor=0 is the only officially supported way to create a non-clustered index within a clustered environment.

References (Splunk Enterprise Documentation):

• indexes.conf Reference – repFactor Attribute Explanation

• Managing Non-Clustered Indexes in Clustered Deployments

• Indexer Clustering: Index Participation and Replication Policies

• Splunk Enterprise Admin Manual – Local-Only and Clustered Index Configurations

The Splunk deployer sends apps to the search head cluster members by default to the path etc/shcluster//default. The deployer is a Splunk component that distributes apps and configurations to members of a search head cluster.

Splunk's documentation recommends placing the configuration bundle in the $SPLUNK_HOME/etc/shcluster/apps directory on the deployer, which then gets distributed to the search head cluster members. However, it should be noted that within each app's directory, configurations can be under default or local subdirectories, with local taking precedence over default for configurations. The reference to etc/shcluster//default is not a standard directory structure and might be a misunderstanding. The correct path where the deployer pushes configuration bundles is $SPLUNK_HOME/etc/shcluster/apps

The Splunk Managing Indexes and Storage Documentation explains that when multiple data sources with significantly different ingestion rates share a single index, index bucket management is governed by volume-based rotation, not by source or time. This means that high-volume data causes buckets to fill and roll more quickly, which in turn causes low-volume data to age out prematurely, even if it is relatively recent — hence Option C is correct.

Additionally, because Splunk organizes data within index buckets based on event time and storage characteristics, low-volume data mixed with high-volume data results in inefficient searches for smaller datasets. Queries that target the low-volume source will have to scan through the same large number of buckets containing the high-volume data, leading to slower-than-necessary search performance — Option B.

Compression efficiency (Option A) and performance optimization through data mixing (Option D) are not influenced by mixing volume patterns; these are determined by the event structure and compression algorithm, not source diversity. Splunk best practices recommend separating data sources into different indexes based on usage, volume, and retention requirements to optimize both performance and lifecycle management.

References (Splunk Enterprise Documentation):

• Managing Indexes and Storage – How Splunk Manages Buckets and Data Aging

• Splunk Indexing Performance and Data Organization Best Practices

• Splunk Enterprise Architecture and Data Lifecycle Management

• Best Practices for Data Volume Segregation and Retention Policies

 The splunk add cluster-master command is used to configure the same search head to join another indexer cluster. A search head can search multiple indexer clusters by adding multiple cluster-master entries in its server.conf file. The splunk add cluster-master command can be used to add a new cluster-master entry to the server.conf file, by specifying the host name and port number of the master node of the other indexer cluster. The splunk add cluster-config command is used to configure the search head to join the first indexer cluster, not the second one. The splunk edit cluster-config command is used to edit the existing cluster configuration of the search head, not to add a new one. The splunk edit cluster-master command does not exist, and it is not a valid command.

 In a distributed environment, knowledge object bundles are replicated from the search head to the SPLUNK_HOME/var/run/searchpeers directory on the search peer(s). A knowledge object bundle is a compressed file that contains the knowledge objects, such as fields, lookups, macros, and tags, that are required for a search. A search peer is a Splunk instance that provides data to a search head in a distributed search. A search head is a Splunk instance that coordinates and executes a search across multiple search peers. When a search head initiates a search, it creates a knowledge object bundle and replicates it to the search peers that are involved in the search. The search peers store the knowledge object bundle in the SPLUNK_HOME/var/run/searchpeers directory, which is a temporary directory that is cleared when the Splunk service restarts. The search peers use the knowledge object bundle to apply the knowledge objects to the data and return the results to the search head. The SPLUNK_HOME/var/lib/searchpeers, SPLUNK_HOME/var/log/searchpeers, and SPLUNK_HOME/var/spool/searchpeers directories are not the locations where the knowledge object bundles are replicated, because they do not exist in the Splunk file system

Decreasing the data model acceleration range will reduce the disk size requirements for a cluster of indexers running Splunk Enterprise Security. Data model acceleration creates tsidx files that consume disk space on the indexers. Reducing the acceleration range will limit the amount of data that is accelerated and thus save disk space. Setting the cluster search factor or replication factor to N-1 will not reduce the disk size requirements, but rather increase the risk of data loss. Increasing the number of buckets per index will also increase the disk size requirements, as each bucket has a minimum size. For more information, see Data model acceleration and Bucket size in the Splunk documentation.

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.