SPLK-2002 Splunk Enterprise Certified Architect Questions and Answers

 The following statements are true regarding Splunk Enterprise performance:

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

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

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

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

This is where in the Job Inspector details can be found to help determine where performance is affected, as it shows the time and resources spent by each component of the search, such as commands, subsearches, lookups, and post-processing1. The Execution Costs > Components section can help identify the most expensive or inefficient parts of the search, and suggest ways to optimize or improve the search performance1. The other options are not as useful as the Execution Costs > Components section for finding performance issues. Option A, Search Job Properties > runDuration, shows the total time, in seconds, that the search took to run2. This can indicate the overall performance of the search, but it does not provide any details on the specific components or factors that affected the performance. Option B, Search Job Properties > runtime, shows the time, in seconds, that the search took to run on the search head2. This can indicate the performance of the search head, but it does not account for the time spent on the indexers or the network. Option C, Job Details Dashboard > Total Events Matched, shows the number of events that matched the search criteria3. This can indicate the size and scope of the search, but it does not provide any information on the performance or efficiency of the search. Therefore, option D is the correct answer, and options A, B, and C are incorrect.

1: Execution Costs > Components 2: Search Job Properties 3: Job Details Dashboard

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

According to the Splunk documentation1, any search head in the cluster can update the KV store collection. The KV store collection is replicated across all the cluster members, and any write operation is delegated to the KV store captain, who then synchronizes the changes with the other members. The KV store primary search head is not a valid term, as there is no such role in a search head cluster. The other options are false because:

The search head cluster captain is not the only node that can update the KV store collection, as any member can initiate a write operation1.

Any search head except the captain can also update the KV store collection, as the write operation will be delegated to the captain1.

This is a mandatory Splunk component when implementing a search head cluster, as it is responsible for distributing the configuration updates and app bundles to the cluster members1. The deployer is a separate instance that communicates with the cluster manager and pushes the changes to the search heads1. The other options are not mandatory components for a search head cluster. Option A, Captain Server, is not a component, but a role that is dynamically assigned to one of the search heads in the cluster2. The captain coordinates the replication and search activities among the cluster members2. Option C, Cluster Manager, is a component for an indexer cluster, not a search head cluster3. The cluster manager manages the replication and search factors, and provides a web interface for monitoring and managing the indexer cluster3. Option D, RAFT Server, is not a component, but a protocol that is used by the search head cluster to elect the captain and maintain the cluster state4. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Use the deployer to distribute apps and configuration updates 2: About the captain 3: About the cluster manager 4: How a search head cluster works

 An indexer clustering requirement is that the cluster members must share the same license pool and license master. A license pool is a group of licenses that are assigned to a set of Splunk instances. A license master is a Splunk instance that manages the distribution and enforcement of licenses in a pool. In an indexer cluster, all cluster members must belong to the same license pool and report to the same license master, to ensure that the cluster does not exceed the license limit and that the license violations are handled consistently. An indexer cluster does not require shared storage, because each cluster member has its own local storage for the index data. An indexer cluster does not have to reside on a dedicated rack, because the cluster members can be located on different physical or virtual machines, as long as they can communicate with each other. An indexer cluster does not have to have at least three members, because a cluster can have as few as two members, although this is not recommended for high availability

 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.

According to the Splunk documentation1, metrics.log is a file that contains various metrics data for reviewing product behavior, such as pipeline, queue, thruput, and tcpout_connections. Metrics.log is stored in the _internal index by default2, which is a special index that contains internal logs and metrics for Splunk Enterprise. The other options are false because:

main is the default index for user data, not internal data3.

_telemetry is an index that contains data collected by the Splunk Telemetry feature, which sends anonymous usage and performance data to Splunk4.

_introspection is an index that contains data collected by the Splunk Monitoring Console, which monitors the health and performance of Splunk components.

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 splunk clean kvstore command is used to clear the KV store. This command will delete all the collections and documents in the KV store and reset it to an empty state. This command can be useful for troubleshooting KV store issues or resetting the KV store data. The splunk clear kvstore, splunk delete kvstore, and splunk reinitialize kvstore commands are not valid Splunk commands. For more information, see Use the CLI to manage the KV store 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

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.

The following logs are included in the _introspection index, which contains data that the Splunk Enterprise deployment logs for platform instrumentation:

disk_objects.log. This log contains information about the disk objects that Splunk creates and manages, such as buckets, indexes, and files. This log can help monitor the disk space usage and the bucket lifecycle.

resource_usage.log. This log contains information about the resource usage of Splunk processes, such as CPU, memory, disk, and network. This log can help monitor the Splunk performance and identify any resource bottlenecks. The following logs are not included in the _introspection index, but rather in the _internal index, which contains data that Splunk generates for internal logging:

audit.log. This log contains information about the audit events that Splunk records, such as user actions, configuration changes, and search activity. This log can help audit the Splunk operations and security.

metrics.log. This log contains information about the performance metrics that Splunk collects, such as data throughput, data latency, search concurrency, and search duration. This log can help measure the Splunk performance and efficiency. For more information, see About Splunk Enterprise logging and [About the _introspection index] in the Splunk documentation.

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

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

The captain is the search head cluster component that is responsible for pushing knowledge bundles to search peers, replicating configuration changes to search head cluster members, and scheduling jobs across the search head cluster. The captain is elected from among the search head cluster members and performs these tasks in addition to serving search requests. The master is the indexer cluster component that is responsible for managing the replication and availability of data across the peer nodes. The deployer is the standalone instance that is responsible for distributing apps and other configurations to the search head cluster members. The deployment server is the instance that is responsible for distributing apps and other configurations to the deployment clients, such as forwarders

The following statements about integrating with third-party systems are true: You can use Splunk alerts to provision actions on a third-party system, and you can forward data from Splunk forwarder to a third-party system without indexing it first. Splunk alerts are triggered events that can execute custom actions, such as sending an email, running a script, or calling a webhook. Splunk alerts can be used to integrate with third-party systems, such as ticketing systems, notification services, or automation platforms. For example, you can use Splunk alerts to create a ticket in ServiceNow, send a message to Slack, or trigger a workflow in Ansible. Splunk forwarders are Splunk instances that collect and forward data to other Splunk instances, such as indexers or heavy forwarders. Splunk forwarders can also forward data to third-party systems, such as Hadoop, Kafka, or AWS Kinesis, without indexing it first. This can be useful for sending data to other data processing or storage systems, or for integrating with other analytics or monitoring tools. A Hadoop application cannot search data in Splunk, because Splunk does not provide a native interface for Hadoop applications to access Splunk data. Splunk can search data in the Hadoop File System (HDFS), but only by using the Hadoop Connect app, which is a Splunk app that enables Splunk to index and search data stored in HDFS

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.

Migration from single-site to multisite index replication only affects new data, not existing data. Multisite policies apply to new data only, meaning that data that is ingested after the migration will follow the multisite replication and search factors. Existing data, or data that was ingested before the migration, will retain the single-site policies, unless they are manually converted to multisite buckets. Single-site buckets do not instantly receive the multisite policies, nor do they automatically convert to multisite buckets. Multisite total values can exceed any single-site factors, as long as they do not exceed the number of peer nodes in the cluster. A master node is not required at each site, only one master node is needed for the entire cluster

 The logical first step when starting a deployment plan is to collect the initial requirements for the deployment from all stakeholders. This includes identifying the business objectives, the data sources, the use cases, the security and compliance needs, the scalability and availability expectations, and the budget and timeline constraints. Collecting the initial requirements helps to define the scope and the goals of the deployment, and to align the expectations of all the parties involved.

Inventorying the currently deployed logging infrastructure, determining what apps and use cases will be implemented, and gathering statistics on the expected adoption of Splunk for sizing are all important steps in the deployment planning process, but they are not the logical first step. These steps can be done after collecting the initial requirements, as they depend on the information gathered from the stakeholders.

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

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

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

Unexpected search behavior or slow performance.

Field extraction or command evaluation errors.

Debugging custom search commands or macros.

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

References (Splunk Enterprise Documentation):

• Search Debugging Techniques and the noop Command

• Understanding search.log and Per-Search Logging Control

• Splunk Search Job Inspector and Debugging Workflow

• Troubleshooting SPL Performance and Field Extraction Issues

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

The __introspection log file contains data about the impact of the Splunk software on the host system, such as CPU, memory, disk, and network usage, as well as KV store performance1. This log file is monitored by default and the contents are sent to the _introspection index1. The other options are not related to the __introspection log file. File monitor input configurations are stored in inputs.conf2. File monitor checkpoint offset is stored in fishbucket3. User activities and knowledge objects are stored in the _audit and _internal indexes respectively4.

 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.

The indexers may have different configurations than the heavy forwarders, which might cause the issue of inconsistently formatted events for a web sourcetype. The heavy forwarders perform parsing and indexing on the data before sending it to the indexers. If the indexers have different configurations than the heavy forwarders, such as different props.conf or transforms.conf settings, the data may be parsed or indexed differently on the indexers, resulting in inconsistent events. The search head configurations do not affect the event formatting, as the search head does not parse or index the data. The data inputs configurations on the forwarders do not affect the event formatting, as the data inputs only determine what data to collect and how to monitor it. The forwarder version does not affect the event formatting, as long as the forwarder is compatible with the indexer. For more information, see [Heavy forwarder versus indexer] and [Configure event processing] in the Splunk documentation.

A multisite indexer cluster is a type of indexer cluster that spans multiple geographic locations or sites. A multisite indexer cluster requires only one cluster manager, also known as the master node, for the entire cluster. The cluster manager is responsible for coordinating the replication and search activities among the peer nodes across all sites. The cluster manager can reside in any site, but it must be accessible by all peer nodes and search heads in the cluster. Option C is the correct answer. Option A is incorrect because having two cluster managers for the entire cluster would introduce redundancy and complexity. Option B is incorrect because having one cluster manager for each site would create separate clusters, not a multisite cluster. Option D is incorrect because having two cluster managers for each site would be unnecessary and inefficient12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Indexer/Multisiteoverview 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Indexer/Clustermanageroverview

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

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s official planning guidance explains that the deployment cycle begins with defining the system architecture, capacity planning, platform design, and topologies. Splunk states that before any component is installed, administrators must complete “infrastructure planning and buildout,” which includes determining indexer capacity, search head roles, clustering strategy, storage layout, and performance requirements. This foundational step ensures that all Splunk components have the proper hardware, network design, and scaling expectations.

After the environment is built, Splunk documentation states that the next stage is “deployment and data onboarding,” which includes configuring indexers, forwarders, parsing rules, event processing pipelines, data source validation, and enrichment steps such as field extractions, tagging, event types, and CIM alignment. Splunk describes this as the phase where you bring in data and confirm correctness, completeness, and normalization.

Only after the system is stable and populated with data does Splunk recommend “user planning and rollout”, which includes developing dashboards, roles, knowledge objects, search best practices, and enabling user access. Splunk emphasizes that user onboarding should occur last, once infrastructure and data pipelines are fully validated.

The following clarification steps should be taken if apps are not appearing on a deployment client:

Check serverclass.conf of the deployment server. This file defines the server classes and the apps and configurations that they should receive from the deployment server. Make sure that the deployment client belongs to the correct server class and that the server class has the desired apps and configurations.

Check deploymentclient.conf of the deployment client. This file specifies the deployment server that the deployment client contacts and the client name that it uses. Make sure that the deployment client is pointing to the correct deployment server and that the client name matches the server class criteria.

Search for relevant events in splunkd.log of the deployment server. This file contains information about the deployment server activities, such as sending apps and configurations to the deployment clients, detecting client check-ins, and logging any errors or warnings. Look for any events that indicate a problem with the deployment server or the deployment client.

Checking the content of SPLUNK_HOME/etc/apps of the deployment server is not a necessary clarification step, as this directory does not contain the apps and configurations that are distributed to the deployment clients. The apps and configurations for the deployment server are stored in SPLUNK_HOME/etc/deployment-apps. For more information, see Configure deployment server and clients in the Splunk documentation.

The average run time of all searches increases as the number of CPU cores on the indexers decreases. The CPU cores are the processing units that execute the instructions and calculations for the data. The number of CPU cores on the indexers affects the search performance, because the indexers are responsible for retrieving and filtering the data from the indexes. The more CPU cores the indexers have, the faster they can process the data and return the results. The less CPU cores the indexers have, the slower they can process the data and return the results. Therefore, the average run time of all searches is inversely proportional to the number of CPU cores on the indexers. The average run time of all searches is not independent of the number of CPU cores on the indexers, because the CPU cores are an important factor for the search performance. The average run time of all searches does not decrease as the number of CPU cores on the indexers decreases, because this would imply that the search performance improves with less CPU cores, which is not true. The average run time of all searches does not increase as the number of CPU cores on the indexers increases, because this would imply that the search performance worsens with more CPU cores, which is not true

When using the props.conf LINE_BREAKER attribute to delimit multi-line events, the SHOULD_LINEMERGE attribute should be set to false. This tells Splunk not to merge events that have been broken by the LINE_BREAKER. Setting the SHOULD_LINEMERGE attribute to true, auto, or none will cause Splunk to ignore the LINE_BREAKER and merge events based on other criteria. For more information, see Configure event line breaking in the Splunk documentation.

 Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching. Search head clustering has the following characteristics:

A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.

At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.

The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.

Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.

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

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

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

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

References (Splunk Enterprise Documentation):

• Search Head Clustering Overview – Minimum Supported Architecture

• Deploy and Configure the Deployer for a Search Head Cluster

• High Availability and Fault Tolerance with RAFT in SHC

According to the Splunk documentation1, the CHARSET setting in props.conf specifies the character set encoding of the source data. This setting has the least impact on indexing performance, as it only affects how Splunk interprets the bytes of the data, not how it processes or transforms the data. The other options are false because:

The SHOULD_LINEMERGE setting in props.conf determines whether Splunk breaks events based on timestamps or newlines. This setting has a significant impact on indexing performance, as it affects how Splunk parses the data and identifies the boundaries of the events2.

The TRUNCATE setting in props.conf specifies the maximum number of characters that Splunk indexes from a single line of a file. This setting has a moderate impact on indexing performance, as it affects how much data Splunk reads and writes to the index3.

The TIME_PREFIX setting in props.conf specifies the prefix that directly precedes the timestamp in the event data. This setting has a moderate impact on indexing performance, as it affects how Splunk extracts the timestamp and assigns it to the event

The index=_internal component=DC* host= search will show all deployment client messages from the universal forwarder. The component field indicates the type of Splunk component that generated the message, and the host field indicates the host name of the machine that sent the message. The index=_audit component=DC* host= search will not return any results, because the deployment client messages are not stored in the _audit index. The index=_internal component=DS* host= search will show the deployment server messages from the deployment server, not the client. The index=_audit component=DS* host= search will also not return any results, for the same reason as above

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

Two indexers clustered is the recommended deployment for a customer who plans to ingest 600 GB of data per day into Splunk, has six concurrent users, and wants high data availability and high search performance. This deployment will provide enough indexing capacity and search concurrency for the customer’s needs, while also ensuring data replication and searchability across the cluster. The customer can also save on the hardware cost by using only two indexers. Two indexers not in a cluster will not provide high data availability, as there is no data replication or failover. Three indexers not in a cluster will provide more indexing capacity and search concurrency, but also more hardware cost and no data availability. The customer’s data retention period, number of long searches, or volume of saved/scheduled searches are not relevant for determining the number of indexers. For more information, see [Reference hardware] and [About indexer clusters and index replication] in the Splunk documentation.

According to the Splunk Search Job Inspector documentation, the search.log file for a completed search job can be accessed through Splunk Web by navigating to the job’s detailed inspection view.

To access it:

Open the completed search in Splunk Web.

Click the Job menu (top right of the search interface).

Select Inspect Job.

In the Job Inspector window, click the search.log link.

This log provides in-depth diagnostic details about how the search was parsed, distributed, and executed across the search head and indexers. It contains valuable performance metrics, command execution order, event sampling information, and any error or warning messages encountered during search processing.

The search.log file is generated for every search job—scheduled, ad-hoc, or background—and is stored in the job’s dispatch directory ($SPLUNK_HOME/var/run/splunk/dispatch//).

Other listed options are incorrect:

Option A queries the _internal index, which does not store per-search logs.

Option B is used to view search configurations, not logs.

Option C is not a valid Splunk Web navigation option.

Thus, the only correct and Splunk-documented method is via Job → Inspect Job → search.log.

References (Splunk Enterprise Documentation):

• Search Job Inspector Overview and Usage

• Analyzing Search Performance Using search.log

• Search Job Management and Dispatch Directory Structure

• Splunk Enterprise Admin Manual – Troubleshooting Searches

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 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

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

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

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

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

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

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

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

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

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 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.

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.

Per the Search Head Clustering (SHC) Deployer Administration Guide, the deployer is responsible for distributing configuration bundles, apps, and baseline settings to all members of a Search Head Cluster (SHC). However, the replication of knowledge objects (Option A) is not handled by the deployer.

Knowledge object replication—covering items such as saved searches, dashboards, lookups, and alerts—is managed internally within the Search Head Cluster using the captain node. The captain coordinates replication among all SHC members using a mechanism called Knowledge Object Replication Framework, which ensures that user-created or runtime configuration changes (e.g., dashboards saved in Splunk Web) are automatically shared across members.

In contrast, the deployer’s primary responsibilities include:

Deploying and updating baseline app configurations (Option B).

Distributing non-replicated, non-runtime configuration updates like props, transforms, and inputs (Option C).

Assisting in the initial migration of apps and configurations into a cluster during setup (Option D).

Therefore, while the deployer handles static configuration management, knowledge object replication is performed dynamically by the SHC itself under captain control, making Option A the correct answer.

References (Splunk Enterprise Documentation):

• Search Head Clustering: How the Deployer Works

• Managing Knowledge Object Replication in Search Head Clusters

• Splunk Enterprise Admin Manual – Deployer vs. Captain Responsibilities

• Distributing Apps and Configurations with the Deployer

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

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

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s multisite clustering documentation describes that search affinity is controlled by the site attribute in server.conf on the search head. Splunk explicitly states that assigning site=site0 on a search head removes site affinity, causing the search head to treat all sites as equal and search remotely as needed. The documentation describes site0 as the special value that disables local-site preference and forces the system to behave like a single-site cluster.

The customer wants each site’s search head to pull results only from its local site. This behavior works only if the search head’s site value matches the local site name (e.g., site1 or site2). By setting it to site0, all locality restrictions are removed, which prevents the desired reduction of network traffic.

The site search factor options (B and D) affect replication and searchable copy placement on indexers, not search head behavior. The number of indexers per site (C) also does not disable search affinity. Therefore only option A disables local-only searching.

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

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