Free SPLK-3003 Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

The Splunk Professional Services (PS) best practice for ingesting network data, such as syslog, is to establish a dedicated data collection tier. This involves using a syslog server (e.g., syslog-ng, rsyslog) to receive the UDP or TCP traffic and write the events to log files on disk. This method provides a persistent buffer, preventing data loss if the downstream Splunk components are unavailable. A Universal Forwarder (UF) is then used to monitor these files. The UF is lightweight, highly scalable, and provides the most reliable data transport by checkpointing its position in the files, ensuring data fidelity and resilience against outages. This architecture decouples the data source from the Splunk indexing tier.

A. Direct network input on a heavy forwarder is not a best practice as it lacks the file-based buffering that prevents data loss during outages.

B. Universal forwarders cannot parse data, and direct UDP input is inherently unreliable, leading to certain data loss if the forwarder is unavailable.

C. While using a syslog server is correct, a heavy forwarder is resource-intensive and not the preferred choice for simple file monitoring; a lightweight universal forwarder is optimal.

1. Splunk Enterprise Documentation, Getting Data In Manual: In the section "Get syslog data into Splunk Enterprise," the documentation explicitly states the best practice: "For production environments, Splunk recommends that you configure a separate syslog server to receive the data, and then write the data to files. You can then use a Splunk universal forwarder to read the files and send the data on to a Splunk indexer." This confirms that an intermediate syslog server writing to files, monitored by a UF, is the recommended architecture for reliability and scalability. (See: Splunk Docs, Getting Data In, "Best practice: Use a dedicated syslog server").

2. Splunk Enterprise Documentation, Forwarding Data Manual: This manual details the roles of different forwarder types. It describes the Universal Forwarder as the best choice for collecting data from file-based sources and forwarding it with minimal resource consumption. It clarifies that UFs do not perform parsing, which should occur on the indexing tier. This supports using a UF over an HF for monitoring the files created by the syslog server. (See: Splunk Docs, Forwarding Data, "Types of forwarders").

The distsearch.conf [replicationBlacklist] stanza prevents specified files, such as a large lookup, from being included in the search bundle sent from the search head to the indexers. Consequently, the indexers cannot find the file when the search is executed, causing an error. To resolve this, the lookup command in the search string must be modified to include the parameter local=true. This parameter forces the lookup operation to be performed on the search head, which has access to the original lookup file. The search head then forwards the results to the indexers, allowing the search to complete successfully without requiring the lookup file on the indexers.

B. The allowcaching setting in transforms.conf is for performance optimization by caching lookup results; it does not resolve the issue of the file being physically absent on the indexers.

C. The lookup command in SPL does not have a blacklist=false parameter. Blacklisting is configured in distsearch.conf, not controlled within a search query.

D. Reverting the change would work but negates the customer's valid objective of reducing the search bundle size. The local=true parameter provides a solution that meets all requirements.

1. Splunk Enterprise Documentation, Distributed Search Manual, "What gets replicated": This document explains the search bundle replication process and the use of blacklisting. It explicitly states, "If you blacklist a lookup table, you must run the lookup on the search head by adding local=true to the lookup command in your search."

Reference: Distributed Search Manual -> Chapter: "Configure distributed search" -> Section: "What gets replicated" -> Subsection: "Exclude files from the bundle".

2. Splunk Enterprise Documentation, Search Reference, "lookup": The official documentation for the lookup command details its parameters.

Reference: Search Reference -> Command: "lookup" -> Syntax section. It defines the local= argument: "If local=true, the lookup is run on the search head. The default is false, which runs the lookup on the indexer."

3. Splunk Enterprise Documentation, Admin Manual, "distsearch.conf": This configuration file reference describes the [replicationBlacklist] stanza.

Reference: Admin Manual -> File Reference: "distsearch.conf" -> Stanza: [replicationBlacklist]. This section confirms that the stanza is used to "prevent specific files or directories from being replicated to the search peers."

In a multi-tiered Splunk architecture, index-time processing, known as parsing, occurs on the first full Splunk Enterprise instance that receives the data. Universal Forwarders (UFs) do not parse data; they only forward it. In the provided diagram, data flows from UFs to Heavy Forwarders (HFs) and then to Indexers. Both HFs and Indexers are full Splunk instances capable of parsing. The parsing configurations (props.conf, transforms.conf) must be placed on the tier designated to perform this task. This could be the HF tier to offload work from the indexers, or it could be the indexer tier itself. Therefore, the configurations must be installed on all instances performing the parsing role.

A. All universal forwarders: Universal forwarders are designed to be lightweight and do not have the parsing pipeline; they cannot process these configurations.

B. Only the indexers: This is incorrect as parsing can be, and often is, configured to occur on an intermediate heavy forwarder tier.

C. All heavy forwarders: This is incorrect because the heavy forwarders could be configured solely for data routing, deferring all parsing to the indexers.

1. Splunk Enterprise Documentation, Getting Data In Manual: In the section "Configuration file locations for this manual," it states, "For index-time configurations that you make in props.conf, transforms.conf, and fields.conf, you must place the files on the Splunk Enterprise instance that performs the parsing. This is usually the indexer, but can be a heavy forwarder if you use one to parse data before it reaches the indexer." This confirms that either HFs or indexers can be the "parsing instance."

2. Splunk Enterprise Documentation, Forwarding Data Manual: The "Compare forwarder types" table explicitly lists that Universal Forwarders do not perform parsing, while Heavy Forwarders do. This supports why option A is incorrect and why HFs (in options C and D) are a potential location for parsing configurations.

3. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers Manual: The chapter on "How indexing works" describes the data pipeline, which includes the parsing phase. This entire pipeline is executed on indexers or, in a tiered architecture, can be executed on heavy forwarders before the data reaches the indexers. This reinforces that the location of parsing is a design choice between HFs and indexers.

The Search Job Inspector is the primary tool for diagnosing the performance of a specific search job. In a distributed environment with an indexer cluster, it provides detailed performance metrics on a per-indexer (peer) basis. This allows an administrator to see how many events each indexer scanned, how much time each indexer spent on the search, and other key statistics. By examining this per-peer data, one can identify "straggler" indexers or data distribution imbalances that are causing the overall search to perform slowly, directly addressing the customer's concern.

B. The Search Job Inspector is a diagnostic tool for analyzing job execution; it does not include a "Search Health Check" feature that automatically provides an optimized version of the SPL query.

C. This is incorrect. The Search Job Inspector is the designated and most effective tool for troubleshooting the performance of a single, specific search job in real-time or after completion.

D. The question does not state that the transaction command is being used. This option makes an unsubstantiated assumption and fails to answer how the Search Job Inspector would be used.

1. Splunk Enterprise Documentation, Search Manual, "Use the Search Job Inspector": This document details the functionality of the Search Job Inspector. The section "View search process activity on the search peers" explicitly states: "The Search Job Inspector shows you information about the search process on each of the search peers that participated in the search." It lists available fields for each peer, including duration (time spent) and scannedCount (events processed), which directly supports the correct answer.

2. Splunk Enterprise Documentation, Search Manual, "Execution costs": This section describes how the inspector breaks down the cost of the search by component. It notes, "For each search processor, see the number of events that the processor read from disk, and the number of events that the processor returned." This confirms the inspector's ability to provide per-component statistics, which in a distributed search includes the individual indexers.

This search is the most efficient because it filters data at the earliest possible stage. By combining the two data sources with a boolean OR and including the specific field-value pairs (status=200, uri=/cart/checkout) directly in the base search, it minimizes the amount of data retrieved from the indexers. This approach ensures only the relevant events are processed by the subsequent stats command, significantly reducing search time and resource consumption compared to alternatives that use less efficient commands or delayed filtering.

A. The append command is less efficient than using OR because it runs a secondary search, adding unnecessary processing overhead to combine the datasets.

C. This is extremely inefficient as it retrieves all events from index=www before appending, processing a much larger dataset than necessary.

D. This search is redundant. It retrieves all data from both indexes first and then filters, which is significantly less efficient than filtering in the base search.

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1. Splunk Enterprise Documentation, Search Manual, "Write better searches": This official guide explicitly states as a best practice: "Filter as early as possible." and "Include time and index filters at the beginning of your search." Option B adheres to this by placing index= and other filtering terms at the start. The guide also advises, "Use the OR operator instead of the append command," which directly contrasts with the inefficient method used in option A. (See section: "Search best practices").

2. Splunk Enterprise Documentation, Search Reference, "append": The documentation for the append command notes its usage and performance characteristics. It is a results-manipulation command that runs after the initial search completes, often involving a resource-intensive subsearch. For simply combining events from different sources for statistical analysis, a single base search with OR is more performant. (See section: "Usage").

3. Splunk Enterprise Documentation, Search Reference, "search": The search command documentation clarifies that the base search (the part before the first pipe) is the most critical for performance. Applying filters in the base search allows the indexers to discard non-matching events. Piping to a second search command, as in option D, forces the search head to process a larger-than-necessary dataset, which is inefficient. (See section: "Description").

The Splunk indexing pipeline processes data in stages, each with a corresponding queue. Inefficient regex replacement transforms are applied during the parsing stage. When these transforms are slow, the parsing processors cannot keep up with the rate of incoming data from the input stage. Consequently, the data waiting to be parsed accumulates, causing the parsingQueue to fill up. This creates a bottleneck at the parsing stage, which in turn starves the downstream queues (such as typing, merging, and indexing) of data, causing them to be underutilized or empty.

A. Typing, merging, parsing, input: Incorrect. The typing and merging queues are downstream from the parsing bottleneck and would be starved for data, not filled.

C. Typing: Incorrect. The typing queue is part of the indexing stage, which occurs after the parsing bottleneck and would not receive enough data to fill up.

D. Indexing, typing, merging, parsing, input: Incorrect. The indexing, typing, and merging queues are all downstream of the slow parsing process and would not fill up.

1. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "How indexing works": This document outlines the data pipeline stages. It describes the parsing stage as the point where Splunk "examines, analyzes, and transforms the data," which includes applying regex transformations defined in transforms.conf. A slowdown in this specific stage directly impacts its preceding queue. (Reference: Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, Version 9.x, Part of the "Get started with indexer clusters" section).

2. Splunk Enterprise Documentation, Monitoring Splunk Enterprise, "Indexing performance dashboard": The official documentation for the Monitoring Console describes the "Indexing Queues" panel. It explicitly states that a high fill percentage for the parsingQueue can indicate a performance issue with parsing, such as "a complex regular expression in a transform." This directly links inefficient regex to a full parsing queue. (Reference: Splunk Enterprise Documentation, Monitoring Splunk Enterprise, Version 9.x, "Dashboards and alerts in the Monitoring Console" -> "Indexing Performance: Instance").

3. Splunk Enterprise Documentation, limits.conf Specification File: The [thruput] stanza in the limits.conf file defines the various queues in the indexing pipeline, including parsingQueue. The documentation describes its function as the queue for the parsing processor. A bottleneck in the processor's ability to perform its tasks (like regex) will cause this specific queue to fill. (Reference: Splunk Enterprise Documentation, Admin Manual, limits.conf -> [thruput] section).

The bloom filter is a data structure used by Splunk to accelerate searches by quickly identifying which index buckets might contain relevant events. This file, named bloomfilter, is a component of each index bucket. It resides at the top level of the bucket's directory structure. The path provided in option A, $SPLUNKHOME/var/lib/splunk/indexfoo/db/db..., represents the specific directory for a warm/cold bucket within the 'indexfoo' index, which is precisely where the bloomfilter file is located.

B. $SPLUNKHOME/var/lib/splunk/indexfoo/db/db155350485815535045078/.tsidx

This path points to the time-series index files (.tsidx), which contain the lexicon and postings lists, not the bloom filter.

C. $SPLUNKHOME/var/lib/splunk/fishbucket

The fishbucket directory tracks the read-state of monitored input files to prevent re-indexing; it is unrelated to index buckets or bloom filters.

D. $SPLUNKHOME/var/lib/splunk/indexfoo/db/db155350485815535045078/rawdata

This directory contains the compressed raw event data (journal.gz), not metadata files like the bloom filter.

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1. Splunk Enterprise Documentation. (2023). Managing Indexers and Clusters of Indexers, Version 9.1.1. "How Splunk stores indexes". In the subsection "Bucket anatomy," the bloomfilter file is explicitly listed as a component within a bucket's main directory. The path structure shown ($SPLUNKHOME/var/lib/splunk///) directly corresponds to the path in option A.

2. Splunk Enterprise Documentation. (2023). Getting Data In, Version 9.1.1. "How the fishbucket works". This section details the purpose of the fishbucket for tracking input file pointers, confirming it is not related to index data structures like bloom filters.

The original search ends with | table clientip, uripath, count. The preceding stats command already generates results in a tabular format containing these exact fields. The final table command is used primarily for presentation—to control the order of columns and ensure no other fields are included. Option C removes this final table command. This is the most efficient rewrite because it eliminates a processing step from the search pipeline that is redundant for generating the core data. The output from stats is sufficient, and removing the unnecessary formatting command optimizes the search.

A: Using the search command after a transforming command like stats is less efficient than using where. The where command is specifically designed to evaluate and filter aggregated results.

B: Placing a table command before the stats command is highly inefficient. It forces Splunk to format all events into a table before the primary aggregation, which is an unnecessary and costly step.

D: Replacing stats with chart does not provide an efficiency gain for this type of aggregation. The stats command is the more direct and standard tool for this task.

1. Splunk Enterprise Documentation, Search Manual, "Write better searches": This guide emphasizes optimizing searches by removing unnecessary commands. The final table command is unnecessary if the default output of the stats command is acceptable. The manual states, "A search is a series of commands... Every command that you add to your search adds to the processing load." (Splunk Enterprise 9.2.1, Search Manual, Chapter: "Write better searches", Section: "Quick tips for optimization").

2. Splunk Enterprise Documentation, Search Reference, "where": The documentation for the where command explicitly contrasts it with search for filtering results of other commands. It states, "The search command can be used for the same purpose, but where is more efficient because it does not use the search parsers." This confirms why Option A is incorrect. (Splunk Enterprise 9.2.1, Search Reference, Command: "where", Section: "Description").

3. Splunk Enterprise Documentation, Search Manual, "About transforming commands and searches": This section explains the nature of transforming commands like stats and table. Placing table before stats (Option B) disrupts the efficient flow of data by performing a formatting transformation before the necessary statistical transformation, which is an anti-pattern for performance. (Splunk Enterprise 9.2.1, Search Manual, Chapter: "About searches", Section: "About transforming commands and searches").

In a Splunk indexer cluster deployment, the recommended best practice is to install and run the Monitoring Console (MC) on the cluster master node. The cluster master is the central point of administration and coordination for all peer nodes (indexers) within the cluster. Hosting the MC on the cluster master provides the most comprehensive and direct visibility into the health, status, and performance of the entire indexer tier, including replication, bucket status, and peer connectivity, without impacting production search workloads.

A. Deployer sharing with master cluster: The deployer's primary role is to manage and distribute configurations to search head cluster members, not to monitor the indexer cluster's operational state.

B. License master that has 50 clients or more: The number of licensed clients is irrelevant to the architectural placement of the Monitoring Console. The decision is based on operational visibility and performance, not licensing metrics.

D. Production Search Head: Installing the MC on a production search head is strongly discouraged as it can consume significant resources, potentially degrading search performance for end-users.

1. Splunk Enterprise Documentation, Monitoring Splunk Enterprise, "Set up the monitoring console in a distributed deployment". In the table under "Which instance should host the monitoring console?", for the "Indexer cluster" deployment type, the recommended location is explicitly stated as the "Cluster master".

2. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "The role of the master node". This section describes the master node's central function in managing the entire cluster, which logically makes it the ideal location for the monitoring tool that oversees the cluster's health.

The core issue is that search heads are waiting for data from indexers across a slow, low-bandwidth WAN link. Splunk's multisite clustering has a built-in feature called "search affinity," where a search head preferentially searches indexers on its local site to minimize such cross-site traffic. However, for search affinity to be effective, a searchable copy of the data must be available on the local site. The sitesearchfactor attribute in server.conf on the cluster manager is used to guarantee that each site maintains a searchable copy of all buckets. By configuring this, local search heads can find the data they need locally, avoiding the network bottleneck and significantly improving search performance. This is a simple configuration change, making it the least expensive and easiest solution.

B. Move all indexers and search heads in one of the data centers into the same site.

This is a major architectural change that would negate the benefits of a multisite deployment for disaster recovery and would not solve the performance issue for users.

C. Install a network pipe with more bandwidth between the two data centers.

While this would likely solve the performance issue, it involves significant hardware and infrastructure costs and is explicitly not the "least expensive" or "easiest" option.

D. Set the site setting on each indexer in the server.conf clustering stanza to be the same for all indexers regardless of site.

This would effectively disable the multisite awareness of the cluster, breaking search affinity entirely and likely worsening performance as searches would be randomly distributed across the slow WAN.

1. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "Configure search affinity in a multisite cluster."

This section states, "For search affinity to work, a searchable copy of the data must reside on the search head's site... You enable search affinity by configuring the sitesearchfactor." It directly identifies that the solution to ensure local data availability for searches is to configure this specific attribute.

2. Splunk Enterprise Documentation, Admin Manual, "server.conf".

Under the [clustering] stanza, the documentation for sitesearchfactor explains its purpose: "Determines the number of searchable copies of buckets that the cluster maintains on each site." This confirms that option A is the correct mechanism to control the location of searchable data copies.

3. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "Multisite indexer cluster deployment overview."

This overview discusses the fundamental reasons for multisite clustering, including disaster recovery and managing geographically dispersed data centers. It notes that search heads on a site primarily search peers on the same site, a concept that relies on the correct configuration of replication and search factors to be effective, especially over a WAN. This context invalidates option D, which would undo this core design principle.

The Splunk Professional Services (PS) preferred method for onboarding data in a production environment is to use configuration files, specifically inputs.conf. This method is highly scalable, repeatable, and allows for version control. In a distributed architecture, inputs.conf files are typically packaged within apps and managed centrally by a deployment server, ensuring consistent configuration across numerous forwarders. This approach provides the robustness and manageability required for enterprise-grade deployments, unlike UI-based methods which are better suited for testing or single-instance setups.

A. UDP is an unreliable, connectionless protocol. It does not guarantee data delivery, making it unsuitable for critical log collection where data loss is unacceptable.

B. The "add data" wizard in Splunk Web is primarily for ad-hoc data inputs, testing, or small-scale, single-instance deployments. It is not a scalable or manageable solution for enterprise environments.

D. A scripted input is a specialized tool for custom data sources (e.g., APIs, databases). Using it to monitor a standard log file is inefficient and overly complex; the monitor:// stanza is the correct and optimized method for that task.

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1. Splunk Enterprise Documentation, Getting Data In, "Configure inputs.conf": This section explicitly states, "The most common way to configure a data input is to add a stanza to an inputs.conf file." It details the process of creating and editing this file as the fundamental method for data onboarding.

2. Splunk Enterprise Documentation, Updating Splunk Enterprise Instances, "About deployment server and forwarder management": This document describes the best practice for managing configurations in a distributed environment. It states, "The deployment server is a tool for distributing configurations, apps, and content to groups of Splunk Enterprise instances... This is the primary way to manage a fleet of universal forwarders." This process relies entirely on managing configuration files like inputs.conf within deployment apps.

3. Splunk Enterprise Documentation, Forwarding Data, "Configure the universal forwarder": This guide outlines the steps for setting up forwarders, emphasizing the creation and deployment of a inputs.conf file to specify which data the forwarder should collect and send. This reinforces that file-based configuration is the standard operational procedure.

A Universal Forwarder (UF) is a lightweight agent designed for minimal resource usage. It reads raw data from a source and forwards it as an unparsed stream to the indexer. It attaches a single set of metadata (e.g., host, source, sourcetype) to the entire data stream or chunk. The indexer is then responsible for the parsing pipeline, which includes event breaking and timestamp extraction.

Conversely, a Heavy Forwarder (HF) is a full Splunk Enterprise instance. It performs the entire parsing pipeline locally before forwarding. It breaks the raw data into individual events, extracts timestamps, and can apply transforms. Consequently, it sends fully "cooked" individual events to the indexer, with each event carrying its own set of metadata. This per-event metadata overhead results in a larger total payload size compared to the UF's stream-based approach.

A: The payload format is fundamentally different (raw stream vs. parsed events), and the HF payload is larger due to per-event metadata, so they are not the same.

C: A Universal Forwarder never performs event breaking. This function is exclusively handled by the parsing pipeline on an indexer or a Heavy Forwarder.

D: This option incorrectly reverses the roles. The UF sends the raw stream with a single metadata set, while the HF sends individual, parsed events.

1. Splunk Enterprise Documentation, Forwarding Data Manual, "Types of forwarders": This section explicitly contrasts the forwarder types. It states, "A universal forwarder is a dedicated, streamlined version of Splunk Enterprise that contains only the essential components needed to forward data... It performs minimal processing on the data... It does not parse the data before forwarding it." For the heavy forwarder, it states, "A heavy forwarder is a full Splunk Enterprise instance... A heavy forwarder parses data before forwarding it." This confirms that the HF sends parsed, individual events while the UF sends an unparsed stream.

2. Splunk Enterprise Documentation, Getting Data In Manual, "The data pipeline": This document describes the stages of data processing. The "Parsing" phase, which breaks the data stream into individual events, occurs on the indexer for data received from a Universal Forwarder. For a Heavy Forwarder, this parsing occurs on the forwarder itself before the data is sent to the "Indexing" pipeline on the indexer. This architectural difference dictates the payload format described in the correct answer.

3. Splunk Enterprise Documentation, Forwarding Data Manual, "How forwarding works": This section details the underlying mechanism. Data from a Universal Forwarder is sent to the receivers/stream endpoint on an indexer, indicating a raw, unparsed stream. Data from a Heavy Forwarder is sent to the receivers/cooked endpoint, signifying that the data has already been processed (parsed) into individual events. This technical distinction directly supports the difference in payload structure.

The Monitoring Console (MC) functions as a dedicated search head to monitor the health and performance of a Splunk environment. To gather data, it must be configured to search all other Splunk instances. In a distributed deployment, this is achieved by adding the instances as search peers. For an indexer cluster, only the cluster master node is added as a search peer to the MC. The cluster master then manages and forwards search requests to the indexer peers. All other single-purpose instances, including standalone search heads, the deployment server, and the license master, must be added individually as search peers to the MC.

A. This is incorrect because it omits the search heads, deployment server, and license master, which are critical components that also require monitoring.

B. This is incorrect because individual indexers within a cluster are not added as search peers. The cluster master manages search coordination for the entire cluster.

D. This is incorrect as it fails to include the search heads and the entire indexer cluster (represented by the cluster master) for comprehensive monitoring.

1. Splunk Enterprise Documentation, Monitoring Splunk Enterprise, Version 9.2.1, "Set up the monitoring console in a distributed deployment". The section "Add instances as search peers" states: "Add all instances in your deployment as search peers to the monitoring console, except for the indexer peers in an indexer cluster. For an indexer cluster, you add the cluster manager node as a search peer." This directly supports adding the cluster master, search heads, deployment server, and license master, while excluding the individual indexers.

2. Splunk Enterprise Documentation, Distributed Search, Version 9.2.1, "How search heads interact with indexer clusters". This section clarifies the search relationship: "To enable a search head to search an indexer cluster, you must configure the cluster master as a search peer on the search head." The Monitoring Console acts as a search head in this context.

Putting an old indexer into manual detention is the correct procedure for planned maintenance, such as a hardware migration. When a peer node is in manual detention, the Cluster Manager is instructed not to initiate bucket-fixing activities for a configurable period (defaulting to 60 minutes). This directly prevents the cluster from starting unnecessary replication operations to create new copies of buckets from the peer being decommissioned. This allows the administrator to shut down the old indexer gracefully without causing a resource-intensive replication storm across the remaining peers in the cluster.

A. Disable the indexing ports on the old indexers.

This only stops new data from being indexed on the peer; it does not prevent the Cluster Manager from initiating bucket fixing for existing data if the peer goes offline.

B. Disable replication ports on the old indexers.

This would cause the Cluster Manager to see the peer as down or unresponsive, which would immediately trigger bucket-fixing activities, increasing replication operations.

D. Put the old indexers into automatic detention.

Automatic detention is a system-triggered state for unstable peers that restart too frequently. It is not a procedure initiated by an administrator for planned maintenance like a migration.

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1. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "Take a peer offline".

This section explicitly details the procedure for planned maintenance. It states, "To take a peer offline for a short time, for maintenance or a configuration change, you can put it in manual detention... When you put a peer in manual detention, the cluster manager does not engage in bucket fixing for that peer's buckets for 60 minutes (by default)... This prevents the cluster from performing unnecessary bucket-fixing activities." This directly supports using manual detention (Option C) to reduce replication.

2. Splunk Enterprise Documentation, Admin Manual, server.conf specification file.

Under the [clustering] stanza, the documentation describes autodetentiononrestartcount. It specifies: "If the peer restarts more than times within the autodetentiononrestartperiod, the cluster manager automatically puts the peer into detention." This confirms that automatic detention (Option D) is a reactive mechanism for unstable peers, not a proactive method for planned migration.