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

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 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

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.

When designing the number and size of indexes, the following considerations should be applied:

Expected daily ingest volumes: This is the amount of data that will be ingested and indexed by the Splunk platform per day. This affects the storage capacity, the indexing performance, and the license usage of the Splunk deployment. The number and size of indexes should be planned according to the expected daily ingest volumes, as well as the peak ingest volumes, to ensure that the Splunk deployment can handle the data load and meet the business requirements12.

Data retention time policies: This is the duration for which the data will be stored and searchable by the Splunk platform. This affects the storage capacity, the data availability, and the data compliance of the Splunk deployment. The number and size of indexes should be planned according to the data retention time policies, as well as the data lifecycle, to ensure that the Splunk deployment can retain the data for the desired period and meet the legal or regulatory obligations13.

Access controls: This is the mechanism for granting or restricting access to the data by the Splunk users or roles. This affects the data security, the data privacy, and the data governance of the Splunk deployment. The number and size of indexes should be planned according to the access controls, as well as the data sensitivity, to ensure that the Splunk deployment can protect the data from unauthorized or inappropriate access and meet the ethical or organizational standards14.

Option D is the correct answer because it reflects the most relevant and important considerations for designing the number and size of indexes. Option A is incorrect because the number of concurrent users is not a direct factor for designing the number and size of indexes, but rather a factor for designing the search head capacity and the search head clustering configuration5. Option B is incorrect because the number of installed apps is not a direct factor for designing the number and size of indexes, but rather a factor for designing the app compatibility and the app performance. Option C is incorrect because it omits the expected daily ingest volumes, which is a crucial factor for designing the number and size of indexes.

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

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

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

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

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