SPLK-2002 Splunk Enterprise Certified Architect Questions and Answers

Splunk Enterprise documentation clearly states that the best method to secure log traffic between Universal Forwarders (UFs) and Indexers is to implement Transport Layer Security (TLS) using signed SSL certificates. When Universal Forwarders send data to Indexers, this communication can be encrypted using SSL/TLS to prevent eavesdropping, data tampering, or interception while in transit.

Splunk provides default self-signed certificates out of the box, but these are only for testing or lab environments and should not be used in production. Production-grade security requires custom, signed SSL certificates — either from an internal Certificate Authority (CA) or a trusted public CA. These certificates validate both the sender (forwarder) and receiver (indexer), ensuring data integrity and authenticity.

In practice, this involves:

Generating or obtaining CA-signed certificates.

Configuring the forwarder’s outputs.conf to use SSL encryption (sslCertPath, sslPassword, and sslRootCAPath).

Configuring the indexer’s inputs.conf and server.conf to require and validate client certificates.

This configuration ensures end-to-end encryption for all log data transmitted from forwarders to indexers.

Routing traffic through a WAF (Option C) does not provide end-to-end encryption for Splunk’s internal communication, and securing search head–to–indexer communication (Option D) is unrelated to forwarder data flow.

References (Splunk Enterprise Documentation):

• Securing Splunk Enterprise: Encrypting Data in Transit Using SSL/TLS

• Configure Forwarder-to-Indexer Encryption

• Server and Forwarder Authentication with Signed Certificates

• Best Practices for Forwarder Management and Security Configuration

The deployer distributes apps and non-search related and manual configuration file changes to the search head cluster members. The deployer does not bootstrap a clean Splunk install for a search head cluster, as this is done by the captain. The deployer also does not distribute runtime knowledge object changes made by users across the search head cluster, as this is done by the replication factor. For more information, see Use the deployer to distribute apps and configuration updates in the Splunk documentation.

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

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

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

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

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

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

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

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

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

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

The sequence typically includes:

Validating cluster state and replication health.

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

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

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

splunk offline --enforce-counts

References (Splunk Enterprise Documentation):

• Indexer Clustering: Decommissioning a Peer Node

• Managing Peer Nodes and Maintaining Data Availability

• Splunk CLI Command Reference – splunk offline

• Cluster Manager and Peer Maintenance Procedures

 Syslog is a standard protocol for sending log messages from various devices and applications to a central server. Syslog can use either UDP or TCP as the transport layer protocol. UDP is faster but less reliable, as it does not guarantee delivery or order of the messages. TCP is slower but more reliable, as it ensures delivery and order of the messages. Therefore, to improve the reliability of syslog delivery to Splunk, it is recommended to use TCP syslog.

Another option to improve the reliability of syslog delivery to Splunk is to use one or more syslog servers to persist data with a Universal Forwarder to send the data to Splunk indexers. This way, the syslog servers can act as a buffer and store the data in case of network or Splunk outages. The Universal Forwarder can then forward the data to Splunk indexers when they are available.

Using a network load balancer to direct syslog traffic to active backend syslog listeners is not a reliable option, as it does not address the possibility of data loss or duplication due to network failures or Splunk outages. Configuring UDP inputs on each Splunk indexer to receive data directly is also not a reliable option, as it exposes the indexers to the network and increases the risk of data loss or duplication due to UDP limitations.

To resync a stale KV Store member in a search head cluster, you need to stop the search head that has the stale KV Store member, run the command splunk clean kvstore --local, and then restart the search head. This triggers the initial synchronization from other KV Store members12.

The command splunk resync kvstore [-source sourceId] is used to resync the entire KV Store cluster from one of the members, not a single member. This command can only be invoked from the node that is operating as search head cluster captain2.

The command splunk clean eventdata -local is used to delete all indexed data from a standalone indexer or a cluster peer node, not to resync the KV Store3.

The 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

 The parallelIngestionPipelines setting can be adjusted on the indexers and forwarders to improve Splunk performance. The parallelIngestionPipelines setting determines how many concurrent data pipelines are used to process the incoming data. Increasing the parallelIngestionPipelines setting can improve the data ingestion and indexing throughput, especially for high-volume data sources. The parallelIngestionPipelines setting can be adjusted on the indexers and forwarders by editing the limits.conf file. The parallelIngestionPipelines setting cannot be adjusted on the search head or the cluster master, because they are not involved in the data ingestion and indexing process.

According to Splunk’s Enterprise Security (ES) Installation and Sizing Guide, the majority of processing and computational load generated by the Splunk App for Enterprise Security is concentrated on the Search Head(s).

This is because Splunk ES is built around a search-driven correlation model — it continuously runs scheduled correlation searches, data model accelerations, and notables generation jobs. These operations rely on the search head tier’s CPU, memory, and I/O resources rather than on indexers. ES also performs extensive data model summarization, CIM normalization, and real-time alerting, all of which are search-intensive operations.

While indexers handle data ingestion and indexing, they are not heavily affected by ES beyond normal search request processing. The Cluster Manager only coordinates replication and plays no role in search execution, and Heavy Forwarders serve as data collection or parsing points with minimal analytical load.

Splunk officially recommends deploying ES on a dedicated Search Head Cluster (SHC) to isolate its high CPU and memory demands from other workloads. For large-scale environments, horizontal scaling via SHC ensures consistent performance and stability.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Security Installation and Configuration Guide

• Search Head Sizing for Splunk Enterprise Security

• Enterprise Security Overview – Workload Distribution and Performance Impact

• Splunk Architecture and Capacity Planning for ES Deployments

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

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

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

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.

According to Splunk’s Deployment Planning and Implementation Guidelines, one of the most critical elements of a Splunk deployment plan is a comprehensive data source inventory and current logging details. This information defines the scope of data ingestion and directly influences sizing, architecture design, and licensing.

A proper deployment plan should identify:

All data sources (such as syslogs, application logs, network devices, OS logs, databases, etc.)

Expected daily ingest volume per source

Log formats and sourcetypes

Retention requirements and compliance constraints

This data forms the foundation for index sizing, forwarder configuration, and storage planning. Without a well-defined data inventory, Splunk architects cannot accurately determine hardware capacity, indexing load, or network throughput requirements.

While stakeholder mapping, topology diagrams, and continuity plans (Options A, B, D) are valuable in a broader IT project, Splunk’s official guidance emphasizes logging details and source inventory as mandatory for a deployment plan. It ensures that the Splunk environment is properly sized, licensed, and aligned with business data visibility goals.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Deployment Planning Manual – Data Source Inventory Requirements

• Capacity Planning for Indexer and Search Head Sizing

• Planning Data Onboarding and Ingestion Strategies

• Splunk Architecture and Implementation Best Practices

According to the Splunk documentation1, a bucket within a clustered index contains two key types of files: the raw data in compressed form (rawdata) and the indexes that point to the raw data (tsidx files). A bucket can be either replicated or searchable, depending on whether it has both types of files or only the rawdata file. A replicated bucket is a bucket that has been copied from one peer node to another for the purpose of data replication. A searchable bucket is a bucket that has both the rawdata and the tsidx files, and can be searched by the search heads. The types of files that exist in a bucket within a clustered index are:

Inside a searchable bucket, there is tsidx and rawdata. This is true because a searchable bucket contains both the data and the index files, and can be searched by the search heads1.

Inside a replicated bucket, there is both tsidx and rawdata. This is true because a replicated bucket can also be a searchable bucket, if it has both the data and the index files. However, not all replicated buckets are searchable, as some of them might only have the rawdata file, depending on the replication factor and the search factor settings1.

The other options are false because:

Inside a replicated bucket, there is only rawdata. This is false because a replicated bucket can also have the tsidx file, if it is a searchable bucket. A replicated bucket only has the rawdata file if it is a non-searchable bucket, which means that it cannot be searched by the search heads until it gets the tsidx file from another peer node1.

Inside a searchable bucket, there is only tsidx. This is false because a searchable bucket always has both the tsidx and the rawdata files, as they are both required for searching the data. A searchable bucket cannot exist without the rawdata file, as it contains the actual data that the tsidx file points to1.

Number of concurrent users: This is an important factor because it affects the search performance and resource utilization of the Splunk environment. More users mean more concurrent searches, which require more CPU, memory, and disk I/O. The number of concurrent users also determines the search head capacity and the search head clustering configuration12

Volume of incoming data: This is another crucial factor because it affects the indexing performance and storage requirements of the Splunk environment. More data means more indexing throughput, which requires more CPU, memory, and disk I/O. The volume of incoming data also determines the indexer capacity and the indexer clustering configuration13

Existence of premium apps: This is a relevant factor because some premium apps, such as Splunk Enterprise Security and Splunk IT Service Intelligence, have additional requirements and recommendations for the Splunk environment. For example, Splunk Enterprise Security requires a dedicated search head cluster and a minimum of 12 CPU cores per search head. Splunk IT Service Intelligence requires a minimum of 16 CPU cores and 64 GB of RAM per search head45

Data retention is optional in the data source assessment because it is not directly related to the ingestion process. Data retention is determined by the index configuration and the storage capacity of the Splunk platform. Data format, data location, and data volume are all essential information for planning how to collect, parse, and index the data source.

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

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

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

[customer_lookup]

replicate = true

field.name = string

field.age = number

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

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

References (Splunk Enterprise Documentation):

• KV Store Collections and Configuration – collections.conf Reference

• Managing KV Store Data in Search Head Clusters

• Automatic Lookup Configuration Using KV Store

• Splunk Enterprise Admin Manual – Distributed KV Store Replication Settings

To activate replication for an index in an indexer cluster, the repFactor attribute must be configured in indexes.conf on all peer nodes. This attribute specifies the replication factor for the index, which determines how many copies of raw data are maintained by the cluster. Setting the repFactor attribute to auto will enable replication for the index. The replicate attribute in indexes.conf is not a valid Splunk attribute. The repFactor attribute in outputs.conf and the replicate attribute in deploymentclient.conf are not related to replication for an index in an indexer cluster. For more information, see Configure indexes for indexer clusters in the Splunk documentation.

When converting from a single-site to a multi-site cluster, existing single-site clustered buckets will maintain replication as required according to the single-site policies, but never age out. Single-site clustered buckets are buckets that were created before the conversion to a multi-site cluster. These buckets will continue to follow the single-site replication and search factors, meaning that they will have the same number of copies and searchable copies across the cluster, regardless of the site. These buckets will never age out, meaning that they will never be frozen or deleted, unless they are manually converted to multi-site buckets. Single-site clustered buckets will not continue to replicate within the origin site, because they will be distributed across the cluster according to the single-site policies. Single-site clustered buckets will not be replicated across all peers in the multi-site cluster, because they will follow the single-site replication factor, which may be lower than the multi-site total replication factor. Single-site clustered buckets will not stop replicating within the single-site and remain on the indexer they reside on, because they will still be subject to the replication and availability rules of the cluster

All of the options are possible causes of a crash in Splunk. According to the Splunk documentation1, incorrect ulimit settings can lead to file descriptor exhaustion, which can cause Splunk to crash or hang. Insufficient disk IOPS can also cause Splunk to crash or become unresponsive, as Splunk relies heavily on disk performance2. Insufficient memory can cause Splunk to run out of memory and crash, especially when running complex searches or handling large volumes of data3. Running out of disk space can cause Splunk to stop indexing data and crash, as Splunk needs enough disk space to store its data and logs4.

1: Configure ulimit settings for Splunk Enterprise 2: Troubleshoot Splunk performance issues 3: Troubleshoot memory usage 4: Troubleshoot disk space issues

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

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

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

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

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

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s Search Head Clustering documentation explains that the cluster uses a majority-based election system. A captain is elected only when a node sees more than half of the cluster. In a two-site design where site1 has the majority of members, Splunk states that the majority site continues normal operation during a network partition. The minority site (site2) is not allowed to elect a captain and should not promote itself.

Splunk specifically warns administrators not to enable static captain on a minority site during a network split. Doing so creates two independent clusters, leading to configuration divergence and severe data-consistency issues. The documentation emphasizes that static captain should only be used for a complete loss of majority, not for a site partition.

Because Site1 maintains majority, it remains the active cluster and site2 does not perform any actions. Splunk states that minority-site members should simply wait until network communication is restored.

Thus the correct answer is B: No action is required.

According to the indexes.conf reference in Splunk Enterprise, the parameter maxDataSize controls the maximum size (in GB or MB) of a single hot bucket before Splunk rolls it to a warm bucket. When the value is set to auto_high_volume on a 64-bit system, Splunk automatically sets the maximum hot bucket size to 10 GB.

The “auto” settings allow Splunk to choose optimized values based on the system architecture:

auto: Default hot bucket size of 750 MB (32-bit) or 10 GB (64-bit).

auto_high_volume: Specifically tuned for high-ingest indexes; on 64-bit systems, this equals 10 GB per hot bucket.

auto_low_volume: Uses smaller bucket sizes for lightweight indexes.

The purpose of larger hot bucket sizes on 64-bit systems is to improve indexing performance and reduce the overhead of frequent bucket rolling during heavy data ingestion. The documentation explicitly warns that these sizes differ on 32-bit systems due to memory addressing limitations.

Thus, for high-throughput environments running 64-bit operating systems, auto_high_volume = 10 GB is the correct and Splunk-documented configuration.

References (Splunk Enterprise Documentation):

• indexes.conf – maxDataSize Attribute Reference

• Managing Index Buckets and Data Retention

• Splunk Enterprise Admin Manual – Indexer Storage Configuration

• Splunk Performance Tuning: Bucket Management and Hot/Warm Transitions

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

 A collection is defined in the collections.conf file, which specifies the name, schema, and permissions of the collection. The kvstore.conf file is used to configure the KV store settings, such as the port, SSL, and replication factor. The other two files do not exist1

As per Splunk Enterprise Search Performance documentation, the most significant factor affecting search performance when querying across a large number of buckets is disk I/O throughput. A search that spans “All Time” forces Splunk to inspect all historical buckets (hot, warm, cold, and potentially frozen if thawed), even if only a few events match the query. This dramatically increases the amount of data read from disk, making the search bound by I/O performance rather than CPU or memory.

Increasing the number of indexing pipelines (Option B) only benefits data ingestion, not search performance. Changing to a real-time search (Option D) does not help because real-time searches are optimized for streaming new data, not historical queries. The indexed_realtime_use_by_default setting (Option C) applies only to streaming indexed real-time searches, not historical “All Time” searches.

To improve performance for such searches, Splunk documentation recommends enhancing disk I/O capability — typically through SSD storage, increased disk bandwidth, or optimized storage tiers. Additionally, creating summary indexes or accelerated data models may help for repeated “All Time” queries, but the most direct improvement comes from faster disk performance since Splunk must scan large numbers of buckets for even small result sets.

References (Splunk Enterprise Documentation):

• Search Performance Tuning and Optimization

• Understanding Bucket Search Mechanics and Disk I/O Impact

• limits.conf Parameters for Search Performance

• Storage and Hardware Sizing Guidelines for Indexers and Search Heads

A 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

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

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

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

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

 Configurations from the deployer are merged into the SPLUNK_HOME/etc/apps/APP_HOME/local directory on the search head cluster member. The deployer distributes apps and other configurations to the search head cluster members in the form of a configuration bundle. The configuration bundle contains the contents of the SPLUNK_HOME/etc/shcluster/apps directory on the deployer. When a search head cluster member receives the configuration bundle, it merges the contents of the bundle into its own SPLUNK_HOME/etc/apps directory. The configurations in the local directory take precedence over the configurations in the default directory. The SPLUNK_HOME/etc/system/local directory is used for system-level configurations, not app-level configurations. The SPLUNK_HOME/etc/apps/search/default directory is used for the default configurations of the search app, not the configurations from the deployer.

 The following artifacts are included in a Splunk diag file:

Internal logs. These are the log files that Splunk generates to record its own activities, such as splunkd.log, metrics.log, audit.log, and others. These logs can help troubleshoot Splunk issues and monitor Splunk performance.

Configuration files. These are the files that Splunk uses to configure various aspects of its operation, such as server.conf, indexes.conf, props.conf, transforms.conf, and others. These files can help understand Splunk settings and behavior. The following artifacts are not included in a Splunk diag file:

OS settings. These are the settings of the operating system that Splunk runs on, such as the kernel version, the memory size, the disk space, and others. These settings are not part of the Splunk diag file, but they can be collected separately using the diag --os option.

Customer data. These are the data that Splunk indexes and makes searchable, such as the rawdata and the tsidx files. These data are not part of the Splunk diag file, as they may contain sensitive or confidential information. For more information, see Generate a diagnostic snapshot of your Splunk Enterprise deployment in the Splunk documentation.

 Splunk internal logs are stored in the SPLUNK_HOME/var/log/splunk directory by default. The default log size for Splunk internal logs is 25 MB, which means that when a log file reaches 25 MB, Splunk rolls it to a backup file and creates a new log file. The default number of backup files is 5, which means that Splunk keeps up to 5 backup files for each log file

According to the Splunk Enterprise Capacity Planning and Hardware Sizing Guidelines, a distributed Splunk environment’s minimum search head specification must ensure that the system can efficiently manage search parsing, ad-hoc query execution, and knowledge object replication. Splunk officially recommends using a 64-bit x86 architecture system with a minimum of two physical CPU cores (or four vCPUs) running at 2 GHz or higher per core for acceptable performance.

Search heads are CPU-intensive components, primarily constrained by processor speed and the number of concurrent searches they must handle. Memory and disk space should scale with user concurrency and search load, but CPU capability remains the baseline requirement. While 128 GB RAM (Option C) is suitable for high-demand or Enterprise Security (ES) deployments, it exceeds the minimum hardware specification for general distributed search environments.

Splunk no longer supports 32-bit architectures (Option B). While a 1Gb Ethernet NIC (Option A) is common, it is not part of the minimum computational specification required by Splunk for search heads. The critical specification is processor capability — two physical cores or equivalent.

References (Splunk Enterprise Documentation):

• Splunk Enterprise Capacity Planning Manual – Hardware and Performance Guidelines

• Search Head Sizing and System Requirements

• Distributed Deployment Manual – Recommended System Specifications

• Splunk Hardware and Performance Tuning Guide

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

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

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

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

According to the Splunk documentation1, the Splunk deployment topology is the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters. The deployment topology defines the number and type of Splunk components, such as forwarders, indexers, search heads, and deployers, that you need to install and configure for your distributed deployment. The deployment topology also determines the network and hardware requirements, the data flow and replication, the high availability and disaster recovery options, and the security and performance considerations for your deployment2. The other options are false because:

Data source inventory is not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as it is a preliminary step that helps you identify the types, formats, locations, and volumes of data that you want to collect and analyze with Splunk. Data source inventory is important for planning your data ingestion and retention strategies, but it does not directly affect the installation and configuration of Splunk components3.

Data policy definitions are not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as they are the rules and guidelines that govern how you handle, store, and protect your data. Data policy definitions are important for ensuring data quality, security, and compliance, but they do not directly affect the installation and configuration of Splunk components4.

Education and training plans are not the part of the deployment plan that is vital prior to installing Splunk indexer clusters and search head clusters, as they are the learning resources and programs that help you and your team acquire the skills and knowledge to use Splunk effectively. Education and training plans are important for enhancing your Splunk proficiency and productivity, but they do not directly affect the installation and configuration of Splunk components5.

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

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

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]

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

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

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

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

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

When Splunk indexes data in a non-clustered environment, it creates index and .tsidx files by default. The index files contain the raw data that Splunk has ingested, compressed and encrypted. The .tsidx files contain the time-series index that maps the timestamps and event IDs of the raw data. The rawdata and index files are not the correct terms for the files that Splunk creates. The compressed and .tsidx files are partially correct, but compressed is not the proper name for the index files. The compressed and meta data files are also partially correct, but meta data is not the proper name for the .tsidx files.

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

The splunk validate cluster-bundle --check-restart command is the officially documented Splunk Enterprise method to determine if a configuration bundle push will trigger a rolling restart within an indexer cluster.

When configuration changes are made on the Cluster Manager (Master Node)—for example, updates to indexes.conf, props.conf, or transforms.conf—Splunk administrators must validate the bundle before pushing it to all peer nodes. Using this command allows the Cluster Manager to simulate the deployment and verify whether the configuration modifications necessitate a restart across peer indexers to take effect.

The --check-restart flag specifically reports whether:

The configuration changes are minor (no restart required).

The changes affect components that require a full or rolling restart (e.g., changes to indexing paths, volume definitions, or replication factors).

Running this validation prior to an actual splunk apply cluster-bundle command prevents service disruption during production operations.

Other commands such as splunk show cluster-bundle-status display deployment status but not restart requirements, and splunk apply cluster-bundle executes the actual deployment, not validation.

References (Splunk Enterprise Documentation):

• Indexer Clustering: Deploy Configuration Bundles with Validation

• splunk validate cluster-bundle Command Reference

• Managing Indexer Clusters – Rolling Restarts and Bundle Deployment Best Practices

• Splunk Enterprise Admin Manual – Cluster Manager Maintenance Commands

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

According to the Splunk documentation1, in a search head cluster, the KV Store Primary is the same node as the search head cluster captain. The KV Store Primary is responsible for coordinating the replication of KV Store data across the cluster members. When any node receives a write request, the KV Store delegates the write to the KV Store Primary. The KV Store keeps the reads local, however. This ensures that the KV Store data is consistent and available across the cluster.

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.

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

 The Splunk instance with the given settings in SPLUNK_HOME/etc/system/local/server.conf is missing the master_uri attribute and needs to be restarted. The master_uri attribute is required for the master node to communicate with the peer nodes and the search head cluster. The master_uri attribute specifies the host name and port number of the master node. Without this attribute, the master node cannot function properly. The Splunk instance also needs to be restarted for the changes in the server.conf file to take effect. The replication_factor setting determines how many copies of each bucket are maintained across the peer nodes. The search factor is a separate setting that determines how many searchable copies of each bucket are maintained across the peer nodes. The search factor is not specified in the given settings, so it defaults to the same value as the replication factor, which is 2. This is not a multi-site cluster, because the site attribute is not specified in the clustering stanza. A multi-site cluster is a cluster that spans multiple geographic locations, or sites, and has different replication and search factors for each site.

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.

Of the following types of files within an index bucket, the rawdata file type may consume the most disk. The rawdata file type contains the compressed and encrypted raw data that Splunk has ingested. The rawdata file type is usually the largest file type in a bucket, because it stores the original data without any filtering or extraction. The bloom filter file type contains a probabilistic data structure that is used to determine if a bucket contains events that match a given search. The bloom filter file type is usually very small, because it only stores a bit array of hashes. The metadata (.data) file type contains information about the bucket properties, such as the earliest and latest event timestamps, the number of events, and the size of the bucket. The metadata file type is also usually very small, because it only stores a few lines of text. The inverted index (.tsidx) file type contains the time-series index that maps the timestamps and event IDs of the raw data. The inverted index file type can vary in size depending on the number and frequency of events, but it is usually smaller than the rawdata file type

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

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

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

The Splunk Capacity Planning Manual defines the total required storage for indexes as a function of daily ingest rate, compression factor, retention period, and an additional padding buffer for index management and growth.

The formula is:

Storage = (Daily Data * Compression Factor * Retention Days) + Padding

Given the values:

Daily rate = 20 GB

Compression factor = 0.5 (50% reduction)

Retention period = 30 days

Padding = 100 GB

Plugging these into the formula gives:

20 * 0.5 * 30 + 100 = 400 GB

This result represents the estimated storage needed to retain 30 days of compressed indexed data with an additional buffer to accommodate growth and Splunk’s bucket management overhead.

Compression factor values typically range between 0.5 and 0.7 for most environments, depending on data type. Using compression in calculations is critical, as indexed data consumes less space than raw input after Splunk’s tokenization and compression processes.

Other options either misapply the compression ratio or the order of operations, producing incorrect totals.

References (Splunk Enterprise Documentation):

• Capacity Planning for Indexes – Storage Sizing and Compression Guidelines

• Managing Index Storage and Retention Policies

• Splunk Enterprise Admin Manual – Understanding Index Bucket Sizes

• Indexing Performance and Storage Optimization Guide

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.

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

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

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