SPLK-2002 Splunk Enterprise Certified Architect Question and Answers

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.

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 splunk add cluster-master command is used to configure the same search head to join another indexer cluster. A search head can search multiple indexer clusters by adding multiple cluster-master entries in its server.conf file. The splunk add cluster-master command can be used to add a new cluster-master entry to the server.conf file, by specifying the host name and port number of the master node of the other indexer cluster. The splunk add cluster-config command is used to configure the search head to join the first indexer cluster, not the second one. The splunk edit cluster-config command is used to edit the existing cluster configuration of the search head, not to add a new one. The splunk edit cluster-master command does not exist, and it is not a valid command.

When using ingest-based licensing, there are no Splunk roles that require the license manager to scale, because the license manager does not need to handle any additional load or complexity. Ingest-based licensing is a new licensing model that allows customers to pay for the data they ingest into Splunk, regardless of the data source, volume, or use case. Ingest-based licensing simplifies the licensing process and eliminates the need for license pools, license stacks, license slaves, and license warnings. The license manager is still responsible for enforcing the license quota and generating license usage reports, but it does not need to communicate with any other Splunk instances or monitor their license usage. Therefore, option C is the correct answer. Option A is incorrect because search peers are indexers that participate in a distributed search. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option B is incorrect because search heads are Splunk instances that coordinate searches across multiple indexers. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option D is incorrect because deployment clients are Splunk instances that receive configuration updates and apps from a deployment server. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/AboutSplunklicensing 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/HowSplunklicensingworks

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

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

KV Store is a feature of Splunk Enterprise that allows apps to store and retrieve data within the context of an app1.

KV Store resides on search heads and replicates data across the members of a search head cluster1.

KV Store resiliency refers to the ability of KV Store to maintain data availability and consistency in the event of failures or disruptions2.

One of the factors that affects KV Store resiliency is the network latency between search heads, which can impact the speed and reliability of data replication2.

Decreasing latency between search heads can improve KV Store resiliency by reducing the chances of data loss, inconsistency, or corruption2.

The other options are not directly related to KV Store resiliency. Faster storage, indexer CPU and memory, and Operations Log size may affect other aspects of Splunk performance, but not KV Store345.

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

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

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

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

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

References (Splunk Enterprise Documentation):

• Search Head Clustering Overview – Minimum Supported Architecture

• Deploy and Configure the Deployer for a Search Head Cluster

• High Availability and Fault Tolerance with RAFT in SHC

A best practice to maximize indexing performance is to minimize configuration generality. Configuration generality refers to the use of generic or default settings for data inputs, such as source type, host, index, and timestamp. Minimizing configuration generality means using specific and accurate settings for each data input, which can reduce the processing overhead and improve the indexing throughput. Using automatic source typing, using the Splunk default settings, and not using pre-trained source types are examples of configuration generality, which can negatively affect the indexing performance

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 Enterprise Capacity Planning Manual states that running Splunk in a virtualized environment typically results in a performance reduction of approximately 20% to 45% compared to equivalent deployments on physical hardware.

This degradation is primarily due to the virtualization overhead inherent in hypervisor environments (such as VMware, Hyper-V, or KVM), which can affect:

Disk I/O throughput and latency — the most critical factor for indexers.

CPU scheduling efficiency, particularly for multi-threaded indexing processes.

Network latency between clustered components.

Splunk’s documentation strongly emphasizes that while virtualized environments offer operational flexibility, they cannot match bare-metal performance, especially under heavy indexing loads.

To mitigate performance loss, Splunk recommends:

Reserving dedicated CPU and I/O resources for Splunk VMs.

Avoiding over-commitment of hardware resources.

Using high-performance SSD storage or paravirtualized disk controllers.

These optimizations can narrow the performance gap, but a 20–45% reduction remains a realistic expectation under typical conditions.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Capacity Planning Manual – Virtualization Performance Considerations

• Splunk on Virtual Infrastructure – Best Practices and Performance Tuning

• Indexer and Search Head Hardware Recommendations

• Performance Testing Guidelines for Splunk Deployments

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

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

The 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

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

The TailingProcessor channel in the splunkd.log file would help troubleshoot this issue, because it contains information about the files that Splunk monitors and indexes, such as the file path, size, modification time, and CRC checksum. It also logs any errors or warnings that occur during the file monitoring process, such as permission issues, file rotation, or file truncation. The TailingProcessor channel can help identify if Splunk is reading the new data from the monitor input file or not, and what might be causing the problem. Option B is the correct answer. Option A is incorrect because the ModularInputs channel logs information about the modular inputs that Splunk uses to collect data from external sources, such as scripts, APIs, or custom applications. It does not log information about the monitor input file. Option C is incorrect because the ChunkedLBProcessor channel logs information about the load balancing process that Splunk uses to distribute data among multiple indexers. It does not log information about the monitor input file. Option D is incorrect because the ArchiveProcessor channel logs information about the archive process that Splunk uses to move data from the hot/warm buckets to the cold/frozen buckets. It does not log information about the monitor input file12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunkd.log 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket#Check_the_splunkd.log_file

The _internal index contains license-related events, such as the license usage, the license quota, the license pool, the license stack, and the license violations. These events are logged by the license manager in the license_usage.log file, which is part of the _internal index. The _audit index contains audit events, such as user actions, configuration changes, and search activity. These events are logged by the audit trail in the audit.log file, which is part of the _audit index. The _license index does not exist in Splunk, as the license-related events are stored in the _internal index. The _introspection index contains platform instrumentation data, such as the resource usage, the disk objects, the search activity, and the data ingestion. These data are logged by the introspection generator in various log files, such as resource_usage.log, disk_objects.log, search_activity.log, and data_ingestion.log, which are part of the _introspection index. For more information, see About Splunk Enterprise logging and [About the _internal index] in the Splunk documentation.

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

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

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

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

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

Splunk Enterprise 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

The correct action to take first if a deployment client is not updating apps is to choose a corrective action based on the splunkd.log of the deployment client. This log file contains information about the communication between the deployment server and the deployment client, and it can help identify the root cause of the problem1. The other actions may or may not help, depending on the situation, but they are not the first steps to take. Choosing a longer phone home interval may reduce the load on the deployment server, but it will also delay the updates for the deployment clients2. Increasing the number of CPU cores or the amount of memory for the deployment server may improve its performance, but it will not fix the issue if the problem is on the deployment client side3. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Troubleshoot deployment server issues 2: Configure deployment clients 3: Hardware and software requirements for the deployment server

The following statements describe licensing in a clustered Splunk deployment: Free licenses do not support clustering, and replicated data does not count against licensing. Free licenses are limited to 500 MB of daily indexing volume and do not allow distributed searching or clustering. To enable clustering, a license with a higher volume limit and distributed features is required. Replicated data is data that is copied from one peer node to another for the purpose of high availability and load balancing. Replicated data does not count against licensing, because it is not new data that is ingested by Splunk. Only the original data that is indexed by the peer nodes counts against licensing. Each cluster member does not require its own clustering license, because clustering licenses are shared among the cluster members. Cluster members must share the same license pool and license master, because the license master is responsible for distributing licenses to the cluster members and enforcing the license limits

 The AggregatorMiningProcessor component in the splunkd.log file will log information related to bad event breaking. The AggregatorMiningProcessor is responsible for breaking the incoming data into events and applying the props.conf settings. If there is a problem with the event breaking, such as incorrect timestamps, missing events, or merged events, the AggregatorMiningProcessor will log the error or warning messages in the splunkd.log file. The Audittrail component logs information about the audit events, such as user actions, configuration changes, and search activity. The EventBreaking component logs information about the event breaking rules, such as the LINE_BREAKER and SHOULD_LINEMERGE settings. The IndexingPipeline component logs information about the indexing pipeline, such as the parsing, routing, and indexing phases. For more information, see About Splunk Enterprise logging and [Configure event line breaking] in the Splunk documentation.

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

 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.

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.

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, 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.

According to Splunk Enterprise Search Performance and Deployment Optimization guidelines, the phone home interval (configured for deployment clients communicating with a Deployment Server) has no impact on search performance.

The phone home mechanism controls how often deployment clients check in with the Deployment Server for configuration updates or new app bundles. This process occurs independently of the search subsystem and does not consume indexer or search head resources that affect query speed, indexing throughput, or search concurrency.

In contrast:

Increasing the number of indexers (Option B) improves search performance by distributing indexing and search workloads across more nodes.

Workload Management (Option C) allows admins to prioritize compute and memory resources for critical searches, optimizing performance under load.

Increasing search heads (Option D) can enhance concurrency and user responsiveness by distributing search scheduling and ad-hoc query workloads.

Therefore, adjusting the phone home interval is strictly an administrative operation and has no measurable effect on Splunk search or indexing performance.

References (Splunk Enterprise Documentation):

• Deployment Server: Managing Phone Home Intervals

• Search Performance Optimization and Resource Management

• Distributed Search Architecture and Scaling Best Practices

• Workload Management Overview – Resource Allocation in Search Operations

Intermediate forwarders are forwarders that receive data from other forwarders and then send that data to indexers. They can be useful in some scenarios, such as when network bandwidth or security constraints prevent direct forwarding to indexers, or when data needs to be routed, cloned, or modified in transit. However, intermediate forwarders also introduce additional complexity and overhead to the data pipeline, which can affect the performance and reliability of data ingestion. Therefore, intermediate forwarders should be avoided when possible, and used only when there is a clear benefit or requirement for them. Some of the drawbacks of intermediate forwarders are:

They increase the number of hops and connections in the data flow, which can introduce latency and increase the risk of data loss or corruption.

They consume more resources on the hosts where they run, such as CPU, memory, disk, and network bandwidth, which can affect the performance of other applications or processes on those hosts.

They require additional configuration and maintenance, such as setting up inputs, outputs, load balancing, security, monitoring, and troubleshooting.

They can create data duplication or inconsistency if they are not configured properly, such as when using cloning or routing rules.

Some of the references that support this answer are:

Configure an intermediate forwarder, which states: “Intermediate forwarding is where a forwarder receives data from one or more forwarders and then sends that data on to another indexer. This kind of setup is useful when, for example, you have many hosts in different geographical regions and you want to send data from those forwarders to a central host in that region before forwarding the data to an indexer. All forwarder types can act as an intermediate forwarder. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”

Intermediate data routing using universal and heavy forwarders, which states: “This document outlines a variety of Splunk options for routing data that address both technical and business requirements. Overall benefits Using splunkd intermediate data routing offers the following overall benefits: … The routing strategies described in this document enable flexibility for reliably processing data at scale. Intermediate routing enables better security in event-level data as well as in transit. The following is a list of use cases and enablers for splunkd intermediate data routing: … Limitations splunkd intermediate data routing has the following limitations: … Increased complexity and resource consumption. splunkd intermediate data routing adds complexity to the data pipeline and consumes resources on the hosts where it runs. This can affect the performance and reliability of data ingestion and other applications or processes on those hosts. Therefore, intermediate routing should be avoided when possible, and used only when there is a clear benefit or requirement for it.”

Use forwarders to get data into Splunk Enterprise, which states: “The forwarders take the Apache data and send it to your Splunk Enterprise deployment for indexing, which consolidates, stores, and makes the data available for searching. Because of their reduced resource footprint, forwarders have a minimal performance impact on the Apache servers. … Note: You can also configure a forwarder to send data to another forwarder, which then sends the data to the indexer. This is called intermediate forwarding. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”

The Splunk deployer sends apps to the search head cluster members by default to the path etc/shcluster//default. The deployer is a Splunk component that distributes apps and configurations to members of a search head cluster.

Splunk's documentation recommends placing the configuration bundle in the $SPLUNK_HOME/etc/shcluster/apps directory on the deployer, which then gets distributed to the search head cluster members. However, it should be noted that within each app's directory, configurations can be under default or local subdirectories, with local taking precedence over default for configurations. The reference to etc/shcluster//default is not a standard directory structure and might be a misunderstanding. The correct path where the deployer pushes configuration bundles is $SPLUNK_HOME/etc/shcluster/apps

According to the Splunk documentation1, the Splunk Deployer deploys app content to the etc/slave-apps/ directory on the search head cluster members by default. This directory contains the apps that the deployer distributes to the members as part of the configuration bundle. The other options are false because:

The etc/apps/ directory contains the apps that are installed locally on each member, not the apps that are distributed by the deployer2.

The etc/shcluster/ directory contains the configuration files for the search head cluster, not the apps that are distributed by the deployer3.

The etc/deploy-apps/ directory is not a valid Splunk directory, as it does not exist in the Splunk file system structure4.

According to Splunk’s Search Scheduler and Job Management documentation, the scheduler.log file, located within the _internal index, records the execution of scheduled and saved searches. This log provides a detailed record of when each search is triggered, how long it runs, and its success or failure status.

Each time a scheduled search runs (for example, alerts, reports, or summary index searches), an entry is written to scheduler.log with fields such as:

sid (search job ID)

app (application context)

savedsearch_name (name of the saved search)

user (owner)

status (success, skipped, or failed)

run_time and result_count

By searching the _internal index for sourcetype=scheduler (or directly viewing scheduler.log), administrators can confirm whether a specific saved search executed as expected and diagnose skipped or delayed runs due to resource contention or concurrency limits.

Other internal logs serve different purposes:

metrics.log records performance metrics.

kvstore.log tracks KV Store operations.

btool.log does not exist — btool outputs configuration data to the console, not a log file.

Hence, scheduler.log is the definitive and Splunk-documented source for validating scheduled search activity.

References (Splunk Enterprise Documentation):

• Saved Searches and Alerts – Scheduler Operation Details

• scheduler.log Reference – Monitoring Scheduled Search Execution

• Monitoring Console: Search Scheduler Health Dashboard

• Troubleshooting Skipped or Delayed Scheduled Searches

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

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

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

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

mode = manager

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

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

mode = peer

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

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

mode = searchhead

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

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

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

References (Splunk Enterprise Documentation):

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

• server.conf Reference – [clustering] Stanza Attributes

• Distributed Search and Cluster Node Role Configuration

• Splunk Enterprise Admin Manual – Cluster Deployment Architecture

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.

 Changing the limits.conf value for max_searches_per_cpu to a higher value is the best option to increase scheduled search capacity on the search head cluster when a large number of searches are skipped across time. This value determines how many concurrent scheduled searches can run on each CPU core of the search head. Increasing this value will allow more scheduled searches to run at the same time, which will reduce the number of skipped searches. Creating a job server on the cluster, running the server.conf captain_is_adhoc_searchhead = true command, or adding another search head to the cluster are not the best options to increase scheduled search capacity on the search head cluster. For more information, see [Configure limits.conf] in the Splunk documentation.

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

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

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

The 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

The correct Splunk deployment to have the recommended minimum components for a high-availability search head cluster is 3 search heads, 1 deployer, 3 indexers. This configuration ensures that the search head cluster has at least three members, which is the minimum number required for a quorum and failover1. The deployer is a separate instance that manages the configuration updates for the search head cluster2. The indexers are the nodes that store and index the data, and having at least three of them provides redundancy and load balancing3. The other options are not recommended, as they either have less than three search heads or less than three indexers, which reduces the availability and reliability of the cluster. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: About search head clusters 2: Use the deployer to distribute apps and configuration updates 3: About indexer clusters and index replication

Splunk Enterprise uses the _audit index to log and store all user activity and audit-related information. This includes details such as user logins, searches executed, configuration changes, role modifications, and app management actions.

The _audit index is populated by data collected from the Splunkd audit logger and records actions performed through both Splunk Web and the CLI. Each event in this index typically includes fields like user, action, info, search_id, and timestamp, allowing administrators to track activity across all Splunk users and components for security, compliance, and accountability purposes.

The _internal index, by contrast, contains operational logs such as metrics.log and scheduler.log used for system performance and health monitoring. _kvstore stores internal KV Store metadata, and _telemetry is used for optional usage data reporting to Splunk.

The _audit index is thus the authoritative source for user behavior monitoring within Splunk environments and is a key component of compliance and security auditing.

References (Splunk Enterprise Documentation):

• Audit Logs and the _audit Index – Monitoring User Activity

• Splunk Enterprise Security and Compliance: Tracking User Actions

• Splunk Admin Manual – Overview of Internal Indexes (_internal, _audit, _introspection)

• Splunk Audit Logging and User Access Monitoring

In the context of splunkd.log events written to the _internal index, the field that identifies the specific log channel is the "channel" field. This information is confirmed by the Splunk Common Information Model (CIM) documentation, where "channel" is listed as a field name associated with Splunk Audit Logs​​.

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.

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

index=_internal sourcetype=splunkd metrics destHost | dedup destHost

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

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

Other listed options are invalid or incorrect because:

sourcetype=internal does not exist.

index=_metrics is not where Splunk stores forwarding telemetry.

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

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

References (Splunk Enterprise Documentation):

• Monitoring Forwarder Connections and Destinations

• Troubleshooting Forwarding Using Internal Logs

• _internal Index Reference – Metrics and destHost Fields

• outputs.conf – Verifying Forwarder Data Routing and Connectivity

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

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.

 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.

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.

The correct answer is B. Set the Replication Factor based on allowed indexer failure. This is a best practice for adding data resiliency to a single-site indexer cluster, as it ensures that there are enough copies of each bucket to survive the loss of one or more indexers without affecting the searchability of the data1. The Replication Factor is the number of copies of each bucket that the cluster maintains across the set of peer nodes2. The Replication Factor should be set according to the number of indexers that can fail without compromising the cluster’s ability to serve data1. For example, if the cluster can tolerate the loss of two indexers, the Replication Factor should be set to three1.

The other options are not best practices for adding data resiliency. Option A, setting the Replication Factor to 49, is not recommended, as it would create too many copies of each bucket and consume excessive disk space and network bandwidth1. Option C, always using the default Replication Factor of 3, is not optimal, as it may not match the customer’s requirements and expectations for data availability and performance1. Option D, setting the Replication Factor based on allowed search head failure, is not relevant, as the Replication Factor does not affect the search head availability, but the searchability of the data on the indexers1. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Configure the replication factor 2: About indexer clusters and index replication

Splunk Enterprise licensing is based on the amount of data that is ingested and indexed by the Splunk platform per day1. The data that counts against the license is the data that is stored in the indexes that are visible to the users and searchable by the Splunk software2. The indexes that are visible and searchable by default are the main index and any custom indexes that are created by the users or the apps3. The main index is the default index where Splunk Enterprise stores all data, unless otherwise specified4.

Option B is the correct answer because the data for the main index will count against the ingest-based license, as it is a visible and searchable index by default. Option A is incorrect because the summary index is a special type of index that stores the results of scheduled reports or accelerated data models, which do not count against the license. Option C is incorrect because the _metrics index is an internal index that stores metrics data about the Splunk platform performance, which does not count against the license. Option D is incorrect because the _introspection index is another internal index that stores data about the impact of the Splunk software on the host system, such as CPU, memory, disk, and network usage, which does not count against the license.

In search head clustering, there are two methods to transfer captaincy to a different member. One method is to use the Search Head Clustering settings menu from Splunk Web on any member. This method allows the user to select a specific member to become the new captain, or to let Splunk choose the best candidate. The other method is to run the splunk transfer shcluster-captain command from the member that the user wants to become the new captain. This method requires the user to know the name of the target member and to have access to the CLI of that member. Using the Monitoring Console is not a method to transfer captaincy, because the Monitoring Console does not have the option to change the captain. Running the splunk transfer shcluster-captain command from the current captain is not a method to transfer captaincy, because this command will fail with an error message

A monitored log file is changing on the forwarder, but Splunk searches are not finding any new data that has been added. This could be caused by two possible reasons: B. An admin has removed the Splunk fishbucket on the forwarder. C. The last 256 bytes of the monitored file are not changing. Option B is correct because the Splunk fishbucket is a directory that stores information about the files that have been monitored by Splunk, such as the file name, size, modification time, and CRC checksum. If an admin removes the fishbucket, Splunk will lose track of the files that have been previously indexed and will not index any new data from those files. Option C is correct because Splunk uses the CRC checksum of the last 256 bytes of a monitored file to determine if the file has changed since the last time it was read. If the last 256 bytes of the file are not changing, Splunk will assume that the file is unchanged and will not index any new data from it. Option A is incorrect because running the splunk clean eventdata -index  command on the indexer will delete all the data from the specified index, but it will not affect the forwarder’s ability to send new data to the indexer. Option D is incorrect because Splunk does not use the first 256 bytes of a monitored file to determine if the file has changed12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Data/Monitorfilesanddirectories 2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket

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