Practice Free SPLK-2002 Splunk Enterprise Certified Architect Exam Questions Answers With Explanation

 When adding or rejoining a member to a search head cluster, and the following error is displayed: Error pulling configurations from the search head cluster captain; consider performing a destructive configuration resync on this search head cluster member.

The corrective action that should be taken is to run the splunk resync shcluster-replicated-config command on this member. This command will delete the existing configuration files on this member and replace them with the latest configuration files from the captain. This will ensure that the member has the same configuration as the rest of the cluster. Restarting the search head, running the splunk apply shcluster-bundle command from the deployer, or running the clean raft command on all members of the search head cluster are not the correct actions to take in this scenario. For more information, see Resolve configuration inconsistencies across cluster members in the Splunk documentation.

Within the [clustering] stanza of the server.conf file, the mode attribute defines the functional role of a Splunk instance within an indexer cluster. Splunk documentation identifies three valid modes:

mode = manager

Defines the node as the Cluster Manager (formerly called the Master Node).

Responsible for coordinating peer replication, managing configurations, and ensuring data integrity across indexers.

mode = peer

Defines the node as an Indexer (Peer Node) within the cluster.

Handles data ingestion, replication, and search operations under the control of the manager node.

mode = searchhead

Defines a Search Head that connects to the cluster for distributed searching and data retrieval.

The value “deployer” (Option C) is not valid within the [clustering] stanza; it applies to Search Head Clustering (SHC) configurations, where it is defined separately in server.conf under [shclustering].

Each mode must be accompanied by other critical attributes such as manager_uri, replication_port, and pass4SymmKey to enable proper communication and security between cluster members.

References (Splunk Enterprise Documentation):

• Indexer Clustering: Configure Manager, Peer, and Search Head Modes

• server.conf Reference – [clustering] Stanza Attributes

• Distributed Search and Cluster Node Role Configuration

• Splunk Enterprise Admin Manual – Cluster Deployment Architecture

 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.

According to the Splunk documentation1, indexer cluster knowledge bundles are the configuration files that the cluster master distributes to the peer nodes as part of the cluster configuration bundle. The knowledge bundles contain the knowledge objects, such as event types, tags, lookups, and so on, that are relevant for indexing and searching the data. The cluster master creates the knowledge bundles by merging the app-name/default and app-name/local directories from the apps that reside on the master node. The cluster master then pushes the knowledge bundles to the peer nodes, where they reside under the $SPLUNK_HOME/var/run directory2. The other options are false because:

Only app-name/local is pushed. This is false because the cluster master pushes both the app-name/default and app-name/local directories, after merging them, to the peer nodes. The app-name/local directory contains the local customizations of the app configuration, while the app-name/default directory contains the default app configuration3.

Only app-name/default is pushed. This is false because the cluster master pushes both the app-name/default and app-name/local directories, after merging them, to the peer nodes. The app-name/default directory contains the default app configuration, while the app-name/local directory contains the local customizations of the app configuration3.

app-name/default and app-name/local are pushed without change. This is false because the cluster master merges the app-name/default and app-name/local directories before pushing them to the peer nodes. This ensures that the peer nodes have the latest and consistent configuration of the apps3.

According to the Splunk documentation1, one of the benefits of search head clustering is the automatic replication of user knowledge objects, such as dashboards, reports, alerts, and tags. This ensures that all cluster members have the same set of knowledge objects and can serve the same search results to the users. The other options are false because:

Allowing indexers to maintain multiple searchable copies of all data is a benefit of indexer clustering, not search head clustering2.

Input settings are not synchronized between search heads, as search head clusters do not collect data from inputs. Data collection is done by forwarders or independent search heads3.

Fewer network ports are not required to be opened between search heads, as search head clusters use several ports for communication and replication among the members4.

Three peers can fail. This is the maximum number of search peers that can fail before data becomes unsearchable in the indexer cluster with the given characteristics. The searchability of the data depends on the Search Factor, which is the number of searchable copies of each bucket that the cluster maintains across the set of peer nodes1. In this case, the Search Factor is 3, which means that each bucket has three searchable copies distributed among the 10 search peers. If three or fewer search peers fail, the cluster can still serve the data from the remaining searchable copies. However, if four or more search peers fail, the cluster may lose some searchable copies and the data may become unsearchable. The other options are not correct, as they either underestimate or overestimate the number of search peers that can fail before data becomes unsearchable. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Configure the search factor

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

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

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

The Monitoring Console (MC) is the Splunk Enterprise monitoring tool that lets you view detailed topology and performance information about your Splunk Enterprise deployment1. The MC can be installed on any Splunk Enterprise instance that can access the data from all the instances in the deployment2. However, following the Splunk recommendations, the MC should be installed on the search head cluster deployer, which is a dedicated instance that manages the configuration bundle for the search head cluster members3. This way, the MC can monitor the search head cluster as well as the indexer cluster and the forwarders, without affecting the performance or availability of the other instances4. The other options are not recommended because they either introduce additional load on the existing instances (such as A and D) or do not have access to the data from the search head cluster (such as B).

1: About the Monitoring Console - Splunk Documentation 2: Add Splunk Enterprise instances to the Monitoring Console 3: Configure the deployer - Splunk Documentation 4: [Monitoring Console setup and use - Splunk Documentation]

The guidance Splunk gives for estimating size on for syslog data is 50% of original data size. This divides between files in the index as follows: rawdata is 15%, tsidx is 35%. The rawdata is the compressed version of the original data, which typically takes about 15% of the original data size. The tsidx is the index file that contains the time-series metadata and the inverted index, which typically takes about 35% of the original data size. The total size of the rawdata and the tsidx is about 50% of the original data size. For more information, see [Estimate your storage requirements] in the Splunk documentation.

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 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.

According to the Splunk documentation1, multi-site clustering is an indexer cluster that spans multiple physical sites, such as data centers. Each site has its own set of peer nodes and search heads. Each site also obeys site-specific replication and search factor rules. The use cases that are made possible by multi-site clustering are:

Greatly reduce WAN traffic by preferentially searching assigned site (search affinity). This means that if you configure each site so that it has both a search head and a full set of searchable data, the search head on each site will limit its searches to local peer nodes. This eliminates any need, under normal conditions, for search heads to access data on other sites, greatly reducing network traffic between sites2.

Seamlessly route searches to a redundant site in case of a site failure. This means that by storing copies of your data at multiple locations, you maintain access to the data if a disaster strikes at one location. Multisite clusters provide site failover capability. If a site goes down, indexing and searching can continue on the remaining sites, without interruption or loss of data2.

The other options are false because:

Use blockchain technology to audit search activity from geographically dispersed data centers. This is not a use case of multi-site clustering, as Splunk does not use blockchain technology to audit search activity. Splunk uses its own internal logs and metrics to monitor and audit search activity3.

Enable a forwarder to send data to multiple indexers. This is not a use case of multi-site clustering, as forwarders can send data to multiple indexers regardless of whether they are in a single-site or multi-site cluster. This is a basic feature of forwarders that allows load balancing and high availability of data ingestion4.

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

Indexed extraction configurations are processed in the indexing phase of the Splunk Enterprise data pipeline. The data pipeline is the process that Splunk uses to ingest, parse, index, and search data. Indexed extraction configurations are settings that determine how Splunk extracts fields from data at index time, rather than at search time. Indexed extraction can improve search performance, but it also increases the size of the index. Indexed extraction configurations are applied in the indexing phase, which is the phase where Splunk writes the data and the .tsidx files to the index. The input phase is the phase where Splunk receives data from various sources and formats. The parsing phase is the phase where Splunk breaks the data into events, timestamps, and hosts. The search phase is the phase where Splunk executes search commands and returns results.

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

Of the following types of files within an index bucket, the rawdata file type may consume the most disk. The rawdata file type contains the compressed and encrypted raw data that Splunk has ingested. The rawdata file type is usually the largest file type in a bucket, because it stores the original data without any filtering or extraction. The bloom filter file type contains a probabilistic data structure that is used to determine if a bucket contains events that match a given search. The bloom filter file type is usually very small, because it only stores a bit array of hashes. The metadata (.data) file type contains information about the bucket properties, such as the earliest and latest event timestamps, the number of events, and the size of the bucket. The metadata file type is also usually very small, because it only stores a few lines of text. The inverted index (.tsidx) file type contains the time-series index that maps the timestamps and event IDs of the raw data. The inverted index file type can vary in size depending on the number and frequency of events, but it is usually smaller than the rawdata file type

The master node distributes configuration bundles to peer nodes in the slave-apps directory under $SPLUNK_HOME/etc. The configuration bundle method is the only supported method for managing common configurations and app deployment across the set of peers. It ensures that all peers use the same versions of these files1. Bundles typically contain a subset of files (configuration files and assets) from $SPLUNK_HOME/etc/system, $SPLUNK_HOME/etc/apps, and $SPLUNK_HOME/etc/users2. The process of distributing knowledge bundles means that peers by default receive nearly the entire contents of the search head’s apps3.

The minimum reference server specification for a Splunk indexer is 12 CPU cores, 12GB RAM, and 800 IOPS. This specification is based on the assumption that the indexer will handle an average indexing volume of 100GB per day, with a peak of 300GB per day, and a typical search load of 1 concurrent search per 1GB of indexing volume. The other specifications are either higher or lower than the minimum requirement. For more information, see [Reference hardware] in the Splunk documentation.

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.

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

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 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.

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.

 A Technical Add-On (TA) is a Splunk app that contains configurations for data collection, parsing, and enrichment. It can also enable event data for a data model, which is useful for creating dashboards and reports. Therefore, before installing a TA, it is important to identify the number of scheduled or real-time searches that will use the data model, and to validate if the TA enables event data for a data model. The number of forwarders that the TA can support is not relevant, as the TA is installed on the indexer or search head, not on the forwarder. The installation location of the TA depends on the type of data and the use case, so it is not a fixed requirement

To expand the search head cluster by adding a new member, node2, the first step is to initialize the cluster configuration on node2 using the splunk init shcluster-config command. This command sets the required parameters for the cluster member, such as the management URI, the replication port, and the shared secret key. The management URI must be unique for each cluster member and must match the URI that the deployer uses to communicate with the member. The replication port must be the same for all cluster members and must be different from the management port. The secret key must be the same for all cluster members and must be encrypted using the splunk _encrypt command. The master_uri parameter is optional and specifies the URI of the cluster captain. If not specified, the cluster member will use the captain election process to determine the captain. Option C shows the correct syntax and parameters for the splunk init shcluster-config command. Option A is incorrect because the splunk bootstrap shcluster-config command is used to bring up the first cluster member as the initial captain, not to add a new member. Option B is incorrect because the master_uri parameter is not required and the mgmt_uri parameter is missing. Option D is incorrect because the splunk add shcluster-member command is used to add an existing search head to the cluster, not to initialize a new member12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/DistSearch/SHCdeploymentoverview#Initialize_cluster_members 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/DistSearch/SHCconfigurationdetails#Configure_the_cluster_members

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

The splunk_instrumentation.log file is the least helpful in troubleshooting a crash, because it contains information about the Splunk Instrumentation feature, which collects and sends usage data to Splunk Inc. for product improvement purposes. This file does not contain any information about the Splunk processes, errors, or crashes. The other options are more helpful in troubleshooting a crash, because they contain relevant information about the Splunk daemon, the standard error output, and the crash report12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunk_instrumentation.log 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunkd_stderr.log

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.

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

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.

The Indexer Discovery feature enables forwarders to dynamically connect to the available peer nodes in an indexer cluster. To use this feature, the manager node must be configured with the [indexer_discovery] stanza and a pass4SymmKey value. The forwarders must also be configured with the same pass4SymmKey value and the master_uri of the manager node. The pass4SymmKey value must be encrypted using the splunk _encrypt command. Therefore, option A indicates that the Indexer Discovery feature has not been fully configured on the manager node, because the pass4SymmKey value is not encrypted. The other options are not related to the Indexer Discovery feature. Option B shows the configuration of a forwarder that is part of an indexer cluster. Option C shows the configuration of a manager node that is part of an indexer cluster. Option D shows an invalid configuration of the [indexer_discovery] stanza, because the pass4SymmKey value is not encrypted and does not match the forwarders’ pass4SymmKey value12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Indexer/indexerdiscovery 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Security/Secureyourconfigurationfiles#Encrypt_the_pass4SymmKey_setting_in_server.conf

 The following artifacts are included in a Splunk diag file:

Internal logs. These are the log files that Splunk generates to record its own activities, such as splunkd.log, metrics.log, audit.log, and others. These logs can help troubleshoot Splunk issues and monitor Splunk performance.

Configuration files. These are the files that Splunk uses to configure various aspects of its operation, such as server.conf, indexes.conf, props.conf, transforms.conf, and others. These files can help understand Splunk settings and behavior. The following artifacts are not included in a Splunk diag file:

OS settings. These are the settings of the operating system that Splunk runs on, such as the kernel version, the memory size, the disk space, and others. These settings are not part of the Splunk diag file, but they can be collected separately using the diag --os option.

Customer data. These are the data that Splunk indexes and makes searchable, such as the rawdata and the tsidx files. These data are not part of the Splunk diag file, as they may contain sensitive or confidential information. For more information, see Generate a diagnostic snapshot of your Splunk Enterprise deployment in the Splunk documentation.

Search is not locked out when a customer has installed a 500GB Enterprise license and a 300GB, no enforcement license on the same license master. The no enforcement license allows the customer to exceed the license quota without locking search, but violations are still recorded. The customer can ingest up to 800GB of data per day without violating the license, but if they ingest more than that, they will incur a violation. However, the violation will not lock search, as the no enforcement license overrides the enforcement policy of the Enterprise license. For more information, see [No enforcement licenses] and [License violations] in the Splunk documentation.

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.

Splunk Enterprise Search Optimization documentation consistently emphasizes that filtering data as early as possible in the search pipeline is the most effective way to improve search performance. The base search (the part before the first pipe |) determines the volume of raw events Splunk retrieves from the indexers. Therefore, by applying restrictive conditions early—such as time ranges, indexed fields, and metadata filters—you can drastically reduce the number of events that need to be fetched and processed downstream.

The best practice is to use indexed field filters (e.g., index=security sourcetype=syslog host=server01) combined with search or where clauses at the start of the query. This minimizes unnecessary data movement between indexers and the search head, improving both search speed and system efficiency.

Using non-streaming commands early (Option C) can degrade performance because they require full result sets before producing output. Likewise, focusing solely on shortening queries (Option A) or excessive use of the not operator (Option D) does not guarantee efficiency, as both may still process large datasets.

Filtering early leverages Splunk’s distributed search architecture to limit data at the indexer level, reducing processing load and network transfer.

References (Splunk Enterprise Documentation):

• Search Performance Tuning and Optimization Guide

• Best Practices for Writing Efficient SPL Queries

• Understanding Streaming and Non-Streaming Commands

• Search Job Inspector: Analyzing Execution Costs

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??.

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 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.

Data Model acceleration is a feature that enables faster searches over large data sets by summarizing the raw data into a more efficient format. Data Model acceleration consumes additional disk space, as it stores both the raw data and the summarized data. The amount of disk space required depends on the size and complexity of the Data Model, the retention period of the summarized data, and the compression ratio of the data. According to the Splunk Enterprise Security Planning and Installation Manual, Data Model acceleration is one of the factors that strongly impacts storage sizing requirements for Enterprise Security. The other factors are the volume and type of data sources, the retention policy of the data, and the replication factor and search factor of the index cluster. The number of scheduled (correlation) searches, the number of Splunk users configured, and the number of source types used in the environment are not directly related to storage sizing requirements for Enterprise Security1

1: https://docs.splunk.com/Documentation/ES/6.6.0/Install/Plan#Storage_sizing_requirements

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.

The Search Job Inspector can be used to expand the following sections: Search job properties and Optimization suggestions. The Search Job Inspector is a tool that provides detailed information about a search job, such as the search parameters, the search statistics, the search timeline, and the search log. The Search Job Inspector can be accessed by clicking the Job menu in the Search bar and selecting Inspect Job. The Search Job Inspector has several sections that can be expanded or collapsed by clicking the arrow icon next to the section name. The Search job properties section shows the basic information about the search job, such as the SID, the status, the duration, the disk usage, and the scan count. The Optimization suggestions section shows the suggestions for improving the search performance, such as using transforming commands, filtering events, or reducing fields. The Execution costs and Saved search history sections are not part of the Search Job Inspector, and they cannot be expanded. The Execution costs section is part of the Search Dashboard, which shows the relative costs of each search component, such as commands, lookups, or subsearches. The Saved search history section is part of the Saved Searches page, which shows the history of the saved searches that have been run by the user or by a schedule

Increasing the number of parallel ingestion pipelines in server.conf is most likely to improve indexing performance when indexing is slow and real-time search results are delayed in a Splunk environment with two indexers and one search head. The parallel ingestion pipelines allow Splunk to process multiple data streams simultaneously, which increases the indexing throughput and reduces the indexing latency. Increasing the maximum number of hot buckets in indexes.conf will not improve indexing performance, but rather increase the disk space consumption and the bucket rolling time. Decreasing the maximum size of the search pipelines in limits.conf will not improve indexing performance, but rather reduce the search performance and the search concurrency. Decreasing the maximum concurrent scheduled searches in limits.conf will not improve indexing performance, but rather reduce the search capacity and the search availability. For more information, see Configure parallel ingestion pipelines in the Splunk documentation.

To determine where a Splunk forwarder is attempting to send its data, administrators can search within the _internal index using the metrics logs generated by the forwarder’s Splunkd process. The correct and documented search is:

index=_internal sourcetype=splunkd metrics destHost | dedup destHost

The _internal index contains detailed operational logs from the Splunkd process, including metrics on network connections, indexing pipelines, and output groups. The field destHost records the destination indexer(s) to which the forwarder is attempting to send data. Using dedup destHost ensures that only unique destination hosts are shown.

This search is particularly useful for troubleshooting forwarding issues, such as connection failures, misconfigurations in outputs.conf, or load-balancing behavior in multi-indexer setups.

Other listed options are invalid or incorrect because:

sourcetype=internal does not exist.

index=_metrics is not where Splunk stores forwarding telemetry.

The field inputHost identifies the source host, not the destination.

Thus, Option D aligns with Splunk’s official troubleshooting practices for forwarder-to-indexer communication validation.

References (Splunk Enterprise Documentation):

• Monitoring Forwarder Connections and Destinations

• Troubleshooting Forwarding Using Internal Logs

• _internal Index Reference – Metrics and destHost Fields

• outputs.conf – Verifying Forwarder Data Routing and Connectivity

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.

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.

A lookup table is a file that contains a list of values that can be used to enrich or modify the data during search time1. Lookup tables can be stored in CSV files or in the KV Store1. Troubleshooting lookup tables involves identifying and resolving issues that prevent the lookup tables from being accessed, updated, or applied correctly by the Splunk searches. Some of the tools and methods that can help with troubleshooting lookup tables are:

web_access.log: This is a file that contains information about the HTTP requests and responses that occur between the Splunk web server and the clients2. This file can help troubleshoot issues related to lookup table permissions, availability, and errors, such as 404 Not Found, 403 Forbidden, or 500 Internal Server Error34.

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 on5. This tool can help troubleshoot issues related to lookup table definitions, locations, and precedence, as well as identify the source of a configuration setting6.

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 performance. This file can help troubleshoot issues related to lookup table commands, arguments, fields, and outputs, such as lookup, inputlookup, outputlookup, lookup_editor, and so on .

Option B is the correct answer because web_access.log is the best place to start troubleshooting lookup table issues, as it can provide the most relevant and immediate information about the lookup table access and status. Option A is incorrect because btool output is not a log file, but a command-line tool. Option C is incorrect because health.log is a file that contains information about the health of the Splunk components, such as the indexer cluster, the search head cluster, the license master, and the deployment server. This file can help troubleshoot issues related to Splunk deployment health, but not necessarily related to lookup tables. Option D is incorrect because configuration_change.log is a file that contains information about the changes made to the Splunk configuration files, such as the user, the time, the file, and the action. This file can help troubleshoot issues related to Splunk configuration changes, but not necessarily related to lookup tables.

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 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

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

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.

The frequency in which a deployment client contacts the deployment server is controlled by the phoneHomeIntervalInSecs attribute in deploymentclient.conf. This attribute specifies how often the deployment client checks in with the deployment server to get updates on the apps and configurations that it should receive. The polling_interval attribute in outputs.conf controls how often the forwarder sends data to the indexer or another forwarder. The polling_interval attribute in deploymentclient.conf and the phoneHomeIntervalInSecs attribute in outputs.conf are not valid Splunk attributes. For more information, see Configure deployment clients and Configure forwarders with outputs.conf in the Splunk documentation.

According to the Splunk documentation1, when migrating an indexer cluster from single-site to multi-site, you must remove the existing single-site attributes from the server.conf file of each peer node. These attributes include replication_factor, search_factor, and cluster_label. You must also restart each peer node after removing the attributes. The other options are false because:

Multi-site policies will apply only to the data created after migration, unless you configure the manager node to convert legacy buckets to multi-site1.

All peer nodes do not need to run the same version of Splunk, as long as they are compatible with the manager node2.

Single-site buckets can be converted to multi-site buckets by changing the constrain_singlesite_buckets setting in the manager node’s server.conf file to "false"1.

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.