Free and Premium Splunk SPLK-2002 Dumps Questions Answers

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.

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

According to the Splunk documentation1, the Search Job Inspector is a tool that you can use to troubleshoot search performance and understand the behavior of knowledge objects, such as event types, tags, lookups, and so on, within the search. You can inspect search jobs that are currently running or that have finished recently. The Search Job Inspector can help you investigate error messages that appear underneath the search bar in Splunk Web, as it can show you the details of the search job, such as the search string, the search mode, the search timeline, the search log, the search profile, and the search properties. You can use this information to identify the cause of the error and fix it2. The other options are false because:

Dashboard panels showing “Waiting for queued job to start” on page load is not a problem that can be investigated using the Search Job Inspector, as it indicates that the search job has not started yet. This could be due to the search scheduler being busy or the search priority being low. You can use the Jobs page or the Monitoring Console to monitor the status of the search jobs and adjust the priority or concurrency settings if needed3.

Different users seeing different extracted fields from the same search is not a problem that can be investigated using the Search Job Inspector, as it is related to the user permissions and the knowledge object sharing settings. You can use the Access Controls page or the Knowledge Manager to manage the user roles and the knowledge object visibility4.

Events not being sorted in reverse chronological order is not a problem that can be investigated using the Search Job Inspector, as it is related to the search syntax and the sort command. You can use the Search Manual or the Search Reference to learn how to use the sort command and its options to sort the events by any field or criteria.

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

The correct topology to ensure a scalable and performant deployment for the customer’s use case is two search head clusters, one for ITSI and one for ES. This configuration provides high availability, load balancing, and isolation for each Splunk app. According to the Splunk documentation1, ITSI and ES should not be installed on the same search head or search head cluster, as they have different requirements and may interfere with each other. Having two separate search head clusters allows each app to have its own dedicated resources and configuration, and avoids potential conflicts and performance issues1. The other options are not recommended, as they either have only one search head or search head cluster, which reduces the availability and scalability of the deployment, or they have both ITSI and ES installed on the same search head or search head cluster, which violates the best practices and may cause problems. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Splunk IT Service Intelligence and Splunk Enterprise Security compatibility

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

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

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

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

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.

 The following statements are true about Splunk indexer clustering:

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

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

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

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

The license_usage.log file is generated and maintained on the License Manager node in a Splunk deployment. This log provides detailed statistics about daily license consumption, including data volume indexed per pool, index, sourcetype, source, and host.

In a distributed or clustered environment (with both search head and indexer clusters), the License Manager acts as the central authority that collects license usage information from all indexers and consolidates it into this log. The License Manager receives periodic reports from each license peer (indexer) and records them in:

$SPLUNK_HOME/var/log/splunk/license_usage.log

The log is automatically indexed into the _internal index with sourcetype=splunkd and can be queried using searches such as:

index=_internal source=*license_usage.log* type="RolloverSummary"

Other components like the Cluster Manager, SHC Deployer, or individual indexers do not store the full consolidated license usage data — they only send summarized reports to the License Manager.

Therefore, the License Manager is the definitive and Splunk-documented location for retrieving and analyzing license_usage.log data across a distributed deployment.

References (Splunk Enterprise Documentation):

• Managing Licenses in a Distributed Environment

• license_usage.log Reference and Structure

• Monitoring License Consumption Using the License Manager

• Splunk Enterprise Admin Manual – License Reporting and Troubleshooting

As a best practice, the internal licensing logs should be stored on the license server. The license server is a Splunk instance that manages the distribution and enforcement of licenses in a Splunk deployment. The license server generates internal licensing logs that contain information about the license usage, violations, warnings, and pools. The internal licensing logs should be stored on the license server itself, because they are relevant to the license server’s role and function. Storing the internal licensing logs on the license server also simplifies the license monitoring and troubleshooting process. The internal licensing logs should not be stored on the indexing layer, the deployment layer, or the search head layer, because they are not related to the roles and functions of these layers. Storing the internal licensing logs on these layers would also increase the network traffic and disk space consumption

The splunk offline --enforce-counts command is the official and documented method used to gracefully decommission a search peer (indexer) from an indexer cluster in Splunk Enterprise. This command ensures that all replication and search factors are maintained before the peer is removed.

When executed, Splunk initiates a controlled shutdown process for the peer node. The Cluster Manager verifies that sufficient replicated copies of all bucket data exist across the remaining peers according to the configured replication_factor (RF) and search_factor (SF). The --enforce-counts flag specifically enforces that replication and search counts remain intact before the peer fully detaches from the cluster, ensuring no data loss or availability gap.

The sequence typically includes:

Validating cluster state and replication health.

Rolling off the peer’s data responsibilities to other peers.

Removing the peer from the active cluster membership list once replication is complete.

Other options like disablepeer, decommission, or remove cluster-peers are not valid Splunk commands. Therefore, the correct documented method is to use:

splunk offline --enforce-counts

References (Splunk Enterprise Documentation):

• Indexer Clustering: Decommissioning a Peer Node

• Managing Peer Nodes and Maintaining Data Availability

• Splunk CLI Command Reference – splunk offline

• Cluster Manager and Peer Maintenance Procedures

According to Splunk Enterprise Distributed Clustering documentation, when you add a search head to an indexer cluster, you must configure it to communicate with the Cluster Manager (previously known as Master Node). The proper way to initialize this connection is by editing the cluster configuration using the splunk edit cluster-config command.

The correct syntax for a search head is:

splunk edit cluster-config -mode searchhead -manager_uri :8089 -secret

Here:

-mode searchhead specifies that this node will function as a search head that participates in distributed search across the indexer cluster.

-manager_uri provides the management URI of the cluster manager.

-secret defines the shared secret key used for secure communication between the manager and cluster members.

Once this configuration is applied, the search head must be restarted for the changes to take effect.

Using -mode peer (Option B) is for indexers joining the cluster, not search heads. The add cluster-manager command (Options C and D) is not a valid Splunk command.

References (Splunk Enterprise Documentation):

• Configure the Search Head for an Indexer Cluster

• Indexer Clustering: Configure the Cluster Manager, Peer, and Search Head Nodes

• Splunk Enterprise Admin Manual: splunk edit cluster-config Command Reference

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

The replication factor and the search factor are two important settings for a Splunk indexer cluster. The replication factor determines how many copies of each bucket are maintained across the set of peer nodes. The search factor determines how many searchable copies of each bucket are maintained. The default values for both settings are 3, which means that each bucket has three copies, and at least one of them is searchable

The master node is the Splunk server role that regulates the functioning of the indexer cluster. The master node coordinates the activities of the peer nodes, such as data replication, data searchability, and data recovery. The master node also manages the cluster configuration bundle and distributes it to the peer nodes. The indexer is the Splunk server role that indexes the incoming data and makes it searchable. The deployer is the Splunk server role that distributes apps and configuration updates to the search head cluster members. The monitoring console is the Splunk server role that monitors the health and performance of the Splunk deployment. For more information, see About indexer clusters and index replication in the Splunk documentation.

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 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

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.

 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.

In Splunk Enterprise, manually editing the serverclass.conf file on a Deployment Server can lead to the loss of UI edit functionality for server classes in Splunk Web.

The Deployment Server manages app distribution to Universal Forwarders and other deployment clients through server classes, which are defined in serverclass.conf. This file maps deployment clients to specific app configurations and defines filtering rules, restart behaviors, and inclusion/exclusion criteria.

When this configuration file is modified manually (outside of Splunk Web), the syntax, formatting, or logical relationships between entries may not match what Splunk Web expects. As a result, Splunk Web may no longer be able to parse or display those server classes correctly. Once this happens, administrators cannot modify deployment settings through the GUI until the configuration file is corrected or reverted to a valid state.

Other files such as deploymentclient.conf, inputs.conf, and deploymentserver.conf control client settings, data inputs, and core server parameters but do not affect the UI-driven deployment management functionality.

Therefore, Splunk explicitly warns administrators in its Deployment Server documentation to use Splunk Web or the CLI when modifying serverclass.conf, and to avoid manual editing unless fully confident in its syntax.

References (Splunk Enterprise Documentation):

• Deployment Server Overview – Managing Server Classes and App Deployment

• serverclass.conf Reference and Configuration Best Practices

• Splunk Enterprise Admin Manual – GUI Limitations After Manual Edits

• Troubleshooting Deployment Server and Serverclass Configuration Issues

The system local directory has the highest precedence among the following directories that contain Splunk configuration files of the same name within different apps. Splunk configuration files are stored in various directories under the SPLUNK_HOME/etc directory. The precedence of these directories determines which configuration file settings take effect when there are conflicts or overlaps. The system local directory, which is located at SPLUNK_HOME/etc/system/local, has the highest precedence among all directories, because it contains the system-level configurations that are specific to the instance. The system default directory, which is located at SPLUNK_HOME/etc/system/default, has the lowest precedence among all directories, because it contains the system-level configurations that are provided by Splunk and should not be modified. The app local directories, which are located at SPLUNK_HOME/etc/apps/APP_NAME/local, have a higher precedence than the app default directories, which are located at SPLUNK_HOME/etc/apps/APP_NAME/default, because the local directories contain the app-level configurations that are specific to the instance, while the default directories contain the app-level configurations that are provided by the app and should not be modified. The app local and default directories have different precedences depending on the ASCII order of the app names, with the app names that come later in the ASCII order having higher precedences.

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

 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

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:

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

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

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

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

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.

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

 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.

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

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

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

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

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

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

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.”

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.

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

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

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

[customer_lookup]

replicate = true

field.name = string

field.age = number

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

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

References (Splunk Enterprise Documentation):

• KV Store Collections and Configuration – collections.conf Reference

• Managing KV Store Data in Search Head Clusters

• Automatic Lookup Configuration Using KV Store

• Splunk Enterprise Admin Manual – Distributed KV Store Replication Settings

Splunk indexing is read/write intensive, as it involves reading data from various sources, writing data to disk, and reading data from disk for searching and reporting. Therefore, it is important to select the appropriate disk storage solution for each deployment, based on the performance, reliability, and cost requirements. The recommended RAID setup for Splunk indexers is RAID 10 (1 + 0), as it provides the best balance of performance and reliability. RAID 10 combines the advantages of RAID 1 (mirroring) and RAID 0 (striping), which means that it offers both data redundancy and data distribution. RAID 10 can tolerate multiple disk failures, as long as they are not in the same mirrored pair, and it can improve the read and write speed, as it can access multiple disks in parallel2

High performance SAN (Storage Area Network) can be used for Splunk indexers, but it is not recommended, as it is more expensive and complex than local disks. SAN also introduces additional network latency and dependency, which can affect the performance and availability of Splunk indexers. SAN is more suitable for Splunk search heads, as they are less read/write intensive and more CPU intensive2

NFS (Network File System) should not be used for storing hot and warm buckets, as it can cause data corruption, data loss, and performance degradation. NFS is a network-based file system that allows multiple clients to access the same files on a remote server. NFS is not compatible with Splunk index replication and search head clustering, as it can cause conflicts and inconsistencies among the Splunk instances. NFS is also slower and less reliable than local disks, as it depends on the network bandwidth and availability. NFS can be used for storing cold and frozen buckets, as they are less frequently accessed and less critical for Splunk operations2

Virtualized environments are not usually preferred over bare metal for Splunk indexers, as they can introduce additional overhead and complexity. Virtualized environments can affect the performance and reliability of Splunk indexers, as they share the physical resources and the network with other virtual machines. Virtualized environments can also complicate the monitoring and troubleshooting of Splunk indexers, as they add another layer of abstraction and configuration. Virtualized environments can be used for Splunk indexers, but they require careful planning and tuning to ensure optimal performance and availability2

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 KV store forms its own cluster within a SHC. The maximum number of SHC members KV store will form is 50. The KV store cluster is a subset of the SHC members that are responsible for replicating and storing the KV store data. The KV store cluster can have up to 50 members, but only 20 of them can be active at any given time. The other members are standby members that can take over if an active member fails. The KV store cluster cannot have more than 50 members, nor can it have an unlimited number of members. The KV store cluster cannot have 25 or 100 members, because these numbers are not multiples of 5, which is the minimum replication factor for the KV store cluster

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

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

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

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

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

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

References (Splunk Enterprise Documentation):

• Securing Data in Transit with SSL/TLS

• Configure Forwarder-to-Indexer Encryption Using SSL Certificates

• Server and Forwarder Authentication Setup Guide

• Splunk Enterprise Admin Manual – Security and Encryption Best Practices

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

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

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.

 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.

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.

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.

The site_mappings attribute should be added to the master node’s server.conf file when decommissioning a site in an indexer cluster. The site_mappings attribute is used to specify how the master node should reassign the buckets from the decommissioned site to the remaining sites. The site_mappings attribute is a comma-separated list of site pairs, where the first site is the decommissioned site and the second site is the destination site. For example, site_mappings = site1:site2,site3:site4 means that the buckets from site1 will be moved to site2, and the buckets from site3 will be moved to site4. The available_sites attribute is used to specify which sites are currently available in the cluster, and it is automatically updated by the master node. The site_search_factor and site_replication_factor attributes are used to specify the number of searchable and replicated copies of each bucket for each site, and they are not affected by the decommissioning process

The default interval at which metrics.log generates a periodic report regarding license utilization is 60 seconds. This report contains information about the license usage and quota for each Splunk instance, as well as the license pool and stack. The report is generated every 60 seconds by default, but this interval can be changed by modifying the license_usage stanza in the metrics.conf file. The other intervals (10 seconds, 30 seconds, and 300 seconds) are not the default values, but they can be set by the administrator if needed. For more information, see About metrics.log and Configure metrics.log in the Splunk documentation.

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

1: transaction command overview

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 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.

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

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

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

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

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

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

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

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

References (Splunk Enterprise Documentation):

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

• Deployer Push Modes and Their Use Cases

• Splunk Enterprise Admin Manual – SHC Deployment Management

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

When building a deployment plan, the architect should perform the following tasks:

Use case checklist. A use case checklist is a document that lists the use cases that the deployment will support, along with the data sources, the data volume, the data retention, the data model, the dashboards, the reports, the alerts, and the roles and permissions for each use case. A use case checklist helps to define the scope and the functionality of the deployment, and to identify the dependencies and the requirements for each use case1

Inventory data sources. An inventory of data sources is a document that lists the data sources that the deployment will ingest, along with the data type, the data format, the data location, the data collection method, the data volume, the data frequency, and the data owner for each data source. An inventory of data sources helps to determine the data ingestion strategy, the data parsing and enrichment, the data storage and retention, and the data security and compliance for the deployment1

Review network topology. A review of network topology is a process that examines the network infrastructure and the network connectivity of the deployment, along with the network bandwidth, the network latency, the network security, and the network monitoring for the deployment. A review of network topology helps to optimize the network performance and reliability, and to identify the network risks and mitigations for the deployment1

Installing Splunk apps is not a task that the architect should perform when building a deployment plan, as it is a task that the administrator should perform when implementing the deployment plan. Installing Splunk apps is a technical activity that requires access to the Splunk instances and the Splunk configurations, which are not available at the planning stage

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.

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.

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

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

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