Free SPLK-2002 Practice Test Questions 2026

Explanation:
When configuring a Splunk indexer cluster, two critical parameters define how data is stored and made searchable:
Replication Factor (RF): The total number of copies of each bucket maintained across the cluster.
Search Factor (SF): The number of searchable copies (with tsidx index files) maintained across the cluster.
By default, Splunk sets RF = 3 and SF = 2. This means every bucket has three copies distributed across peers, and two of those copies are searchable. This default strikes a balance between redundancy and storage efficiency.

Why C is Correct

Replication Factor = 3: Ensures that each bucket has three copies across the cluster. This provides resilience against node failures. If one peer goes down, the cluster still has two copies available.

Search Factor = 2: Out of the three copies, two are searchable. Searchable copies contain tsidx files, which allow searches to run. Having two searchable copies ensures redundancy for search operations without requiring all copies to be searchable, which would increase storage overhead.
Operational balance: This default configuration is designed to provide fault tolerance while minimizing unnecessary disk usage.

Why the other options are not correct

A. RF=2, SF=2 → Incorrect
With only two copies, redundancy is limited. If one peer fails, only one copy remains, which is risky for enterprise deployments.
Splunk defaults to higher redundancy (RF=3) to ensure data durability.
While valid, this is not the default configuration.

B. RF=2, SF=3 → Incorrect
This is an invalid configuration because the search factor cannot exceed the replication factor. You cannot have three searchable copies if only two total copies exist.
Splunk enforces that SF ≤ RF.

C. RF=3, SF=2 → Correct
Matches Splunk’s documented defaults.
Provides three total copies for durability and two searchable copies for redundancy in search operations.

D. RF=3, SF=3 → Incorrect
While valid, this is not the default. It would mean all three copies are searchable, which increases storage overhead unnecessarily.
Splunk defaults to SF=2 to balance search redundancy with storage efficiency.

References
Splunk Docs – About indexer clusters
Splunk Docs – Replication and search factors

Explanation:
In Splunk, indexing performance can be impacted not only by CPU and memory but also by resource contention between indexing and searching. Even if indexers have ample CPU and memory, heavy search activity (especially concurrent scheduled searches) can delay indexing and real-time search results because both indexing and searching compete for I/O and pipeline resources.

By decreasing the maximum concurrent scheduled searches in limits.conf, you reduce the search workload competing with indexing. This frees up resources for ingestion pipelines, improving indexing throughput and reducing delays in real-time search results.

Why the other options are not correct

A. Increase the maximum number of hot buckets in indexes.conf → Incorrect
Increasing hot buckets affects how data is rolled into warm buckets, but it does not directly improve indexing speed. It mainly influences bucket management and retention, not ingestion throughput.

B. Increase the number of parallel ingestion pipelines in server.conf → Incorrect
Parallel ingestion pipelines can improve indexing throughput, but only if indexing is CPU-bound. In this scenario, CPU and memory are already ample, so the bottleneck is not ingestion pipelines but search contention.

C. Decrease the maximum size of the search pipelines in limits.conf → Incorrect
Reducing search pipeline size does not improve indexing. It may even degrade search performance. This setting controls search execution, not ingestion speed.

D. Decrease the maximum concurrent scheduled searches in limits.conf → Correct
This directly reduces search load competing with indexing, allowing ingestion to proceed faster.

References
Splunk Docs – Tune search performance with limits.conf
Splunk Docs – Indexer performance and resource contention
Splunk Docs – Improve indexing throughput

Explanation:

✅Correct Answer (A):
TCP syslog provides a connection-oriented protocol that guarantees delivery. Unlike UDP, TCP includes acknowledgment of received packets and will retransmit any lost data. This built-in reliability mechanism ensures no syslog messages are lost in transit due to network issues.

✅Correct Answer (C):
A network load balancer acts as a single, highly available endpoint for syslog traffic. It can distribute connections across multiple backend receivers (like heavy forwarders or indexers) and automatically reroute traffic if a receiver fails, preventing a single point of failure and improving overall system resilience.

✅Correct Answer (D):
This method introduces a persistent queue. The syslog server writes data to disk, and the Universal Forwarder then reliably tails these files, handling backpressure and network interruptions. This two-stage process protects against data loss if Splunk components are temporarily unavailable.

❌Incorrect Answer:
B: Configuring UDP inputs directly on indexers is not a reliable method. UDP is a connectionless protocol with no delivery guarantees, and sending data directly to indexers creates a single point of failure. If an indexer is busy or fails, syslog data sent via UDP will be lost permanently.

Reference:
Splunk documentation on configuring syslog inputs recommends TCP for reliable data transmission
Splunk architectural guidance often recommends load balancers for high-availability data ingestion paths.
Splunk best practices for data collection describe using intermediate syslog servers with forwarders as a robust ingestion pattern

Explanation:

In a distributed Splunk environment, when search heads send knowledge object bundles (containing configurations, lookups, macros, etc.) to search peers (indexers), those bundles are replicated to a specific directory on the peers.

That directory is: $SPLUNK_HOME/var/run/searchpeers
This location is used by search peers to store the replicated bundles temporarily so they can apply the knowledge objects during searches.

Why the other options are not correct

A. $SPLUNK_HOME/var/lib/searchpeers → Incorrect The var/lib path is used for persistent storage of Splunk’s internal data, not for replicated search bundles.

B. $SPLUNK_HOME/var/log/searchpeers → Incorrect The var/log path is reserved for log files. Search bundles are not stored here.

C. $SPLUNK_HOME/var/run/searchpeers → Correct This is the documented location where search peers receive and store replicated knowledge object bundles from the search head.

D. $SPLUNK_HOME/var/spool/searchpeers → Incorrect The var/spool directory is used for queued data waiting to be processed, not for search head bundle replication.

References
Splunk Docs – Distributed search overview

Explanation:

A multi‑site indexer cluster in Splunk can be configured in two supported ways:

A. Via Splunk Web → Correct
Splunk Web provides a guided interface to configure clustering, including multi‑site settings such as site_replication_factor and site_search_factor. This is a supported and recommended method for administrators who prefer UI‑based configuration.

B. Directly edit $SPLUNK_HOME/etc/system/local/server.conf → Correct
You can manually configure clustering by editing the server.conf file under the local directory. This is where you define attributes like mode, multisite, site_replication_factor, and site_search_factor. Editing the local configuration file is supported and overrides defaults.

Why the other options are not correct

C. Run a splunk edit cluster-config command from the CLI → Incorrect
Splunk does not provide a CLI command called splunk edit cluster-config. Cluster configuration must be done either through Splunk Web or by editing configuration files.

D. Directly edit $SPLUNK_HOME/etc/system/default/server.conf → Incorrect
The default directory contains Splunk’s baseline configuration files. These should never be edited directly, as they are overwritten during upgrades. All custom cluster settings must be placed in the local directory.

References
Splunk Docs – Configure multisite indexer clusters
Splunk Docs – Indexer cluster configuration overview

Explanation:

In Splunk’s deployment server architecture, deployment clients (such as forwarders or other Splunk instances) periodically “phone home” to the deployment server to check for new or updated configuration bundles. The frequency of this communication is controlled by the phoneHomeIntervalInSecs attribute in the deploymentclient.conf file.
By default, the value is 300 seconds (5 minutes), meaning each client contacts the deployment server every five minutes unless configured otherwise. This setting ensures that clients remain up to date with the latest configurations while balancing network and server load.

Why D is Correct
phoneHomeIntervalInSecs in deploymentclient.conf is the documented attribute that controls how often a deployment client contacts the deployment server.

It directly defines the polling interval in seconds.

Adjusting this value allows administrators to fine‑tune how frequently clients check in, depending on environment size and update frequency.

Why the other options are not correct

A. polling_interval in outputs.conf → Incorrect
outputs.conf controls forwarding behavior (where and how data is sent to indexers or other Splunk instances).
It does not manage deployment server communication.
The polling_interval attribute is unrelated to deployment client check‑in frequency.

B. phoneHomeIntervalInSecs in outputs.conf → Incorrect
While the attribute name looks correct, it does not exist in outputs.conf.
outputs.conf is strictly for data forwarding configuration, not deployment client management.

C. polling_interval in deploymentclient.conf → Incorrect
The correct attribute in deploymentclient.conf is phoneHomeIntervalInSecs, not polling_interval.
Using polling_interval here would have no effect, as it is not a valid setting for deployment clients.

D. phoneHomeIntervalInSecs in deploymentclient.conf → Correct
This is the exact attribute and file documented by Splunk for controlling deployment client communication frequency.
It is the only valid option among the four.

References
Splunk Docs – deploymentclient.conf
Splunk Docs – About deployment clients and servers

Explanation:

In Splunk’s deployment server model, the serverclass.conf file defines how configuration bundles are distributed to deployment clients. To control which clients receive a particular bundle, administrators use client filters. These filters allow precise targeting of clients based on their identity or network attributes.

The two valid client filters are:

DNS name (A)
Splunk supports filtering clients by their DNS hostname. This is useful when clients follow a naming convention (e.g., web01.company.com, db02.company.com). Administrators can group clients by hostname patterns to ensure only specific systems receive certain configurations.

IP address (B)
Splunk also supports filtering clients by IP address or subnet. This is commonly used to target clients in specific networks or geographic locations. For example, you can configure a server class to apply only to clients in the 10.0.0.0/24 subnet.
These two filters are the documented and supported methods for client targeting in serverclass.conf.

Why the other options are not correct

C. Splunk server role → Incorrect
Splunk does not use “server role” as a filter in serverclass.conf. Roles such as “indexer” or “search head” are architectural concepts, not attributes available for client filtering. There is no configuration directive in serverclass.conf that allows filtering by role.

D. Platform (machine type) → Incorrect
While Splunk can detect operating system or platform type internally, this is not a supported filter in serverclass.conf. Client filters are limited to DNS and IP. Platform type cannot be used to control bundle distribution.

Why A and B are Correct
Both DNS name and IP address are explicitly documented as valid client filters in serverclass.conf.
They provide administrators with flexible ways to group and target deployment clients.
These filters ensure that only the intended clients receive specific configuration bundles, reducing risk and improving manageability in large environments.

References
Splunk Docs – serverclass.conf
Splunk Docs – About deployment server

Explanation:

In a Splunk search head cluster (SHC), the captain is the elected leader responsible for coordinating searches, managing knowledge object replication, and maintaining cluster stability. The process of electing and maintaining captaincy must be fault‑tolerant, consistent, and reliable across distributed nodes. Splunk achieves this by using the Raft distributed consensus algorithm.

Raft is a consensus algorithm designed to be easier to understand than Paxos while still providing strong guarantees of consistency. It ensures that all members of the search head cluster agree on which node is the captain, even in the presence of failures. Raft provides fault tolerance by allowing the cluster to continue operating as long as a majority of members are available. This is critical in Splunk environments where search head clusters must remain highly available and consistent.

Why the other options are not correct

B. Rapt distributed consensus → Incorrect
“Rapt” is a distractor. There is no such algorithm in distributed systems. Splunk documentation does not reference this term. It is included as a misleading option to test whether candidates can distinguish between the real Raft algorithm and fabricated names.

C. Rift distributed consensus → Incorrect
“Rift” is also a distractor. Splunk does not use this algorithm for captain election. It is not a recognized consensus mechanism in distributed computing. The exam expects you to know that only Raft is valid and documented.

D. Round‑robin distribution consensus → Incorrect
Round‑robin is a scheduling method, not a consensus algorithm. It cannot guarantee consistency or fault tolerance in leader election. Using round‑robin would not provide the reliability required for Splunk’s search head clustering. Consensus requires majority agreement, not simple rotation.

Why A is Correct
Splunk explicitly documents that Raft distributed consensus is used for captain election in search head clusters.

Raft ensures strong consistency and fault tolerance, which are essential in distributed search environments.

It is a proven algorithm widely adopted in distributed systems for leader election and log replication.

Raft is designed to be understandable and practical, making it suitable for Splunk’s distributed architecture.

References
Splunk Docs – Search head clustering overview
Splunk Docs – Captain election in search head clusters

Explanation :

✅Correct Answer: A
When you migrate from a single-site to a multi-site cluster, the new multi-site replication policy only applies to new data buckets created after the configuration change. Existing data buckets that were created under the single-site configuration continue to be managed exclusively by the original single-site replication_factor and search_factor. They will continue to be maintained and replicated only within their original site according to these old rules until they age out and are deleted by Splunk's normal retention policies.

❌Incorrect Answers:

B: It is incorrect that the buckets "never age out." They are still subject to the standard data aging and retention policies based on their age and the configured retirement period. Splunk Docs on data aging.

C: Existing single-site buckets are not automatically redistributed or replicated across the new multi-site topology. They remain confined to the peers in their original site as per their initial configuration. Splunk Docs on migration behavior.

D: The buckets do not stop replicating. The cluster master continues to enforce the original single-site replication policy for these pre-existing buckets to ensure their high availability and data integrity within their original site. Splunk Docs on how the cluster master manages buckets.

Reference:
The Splunk documentation on migrating from single-site to multi-site explicitly states, "The site-based factors apply only to data that is indexed after you make the configuration change. Existing data continues to be governed by the original replication and search factors."

Explanation:

In Splunk, each index bucket contains several types of files that together make up the searchable data structure. The most significant of these is the rawdata file, which stores the actual ingested event text. Because it contains the complete representation of every event, rawdata files are typically the largest component of a bucket and consume the most disk space.

When Splunk indexes data, it writes the original event text into the rawdata file. This ensures durability and allows Splunk to rebuild other supporting files if needed. The rawdata file grows in proportion to the volume of ingested data, making it the primary driver of disk usage in any index bucket.

Why A is Correct
Rawdata files contain the full event text exactly as ingested.
They are essential for durability and recovery.
Their size scales directly with ingestion volume, meaning they consume the most disk space compared to other supporting files.
Splunk documentation confirms that rawdata is the largest component of a bucket.

Why the other options are not correct

B. Bloom filter → Incorrect
Bloom filters are small, space‑efficient probabilistic data structures used to quickly eliminate non‑matching events during searches. They are designed to minimize disk usage. While useful for search optimization, they consume negligible disk compared to rawdata.

C. Metadata (.data) → Incorrect
Metadata files store bucket attributes such as time ranges, high‑level statistics, and state information. These files are small and lightweight. They do not scale with ingestion volume in the same way rawdata does, so they never consume the most disk.

D. Inverted index (.tsidx) → Incorrect
Tsidx files store tokenized terms mapped to events, enabling fast search. They can be sizable, especially in large environments, but they are still smaller than rawdata files. Tsidx files are built from rawdata and optimized for search, but they do not surpass rawdata in disk consumption.

References
Splunk Docs – Buckets and index files

Explanation:
In a Splunk search head cluster (SHC), scheduled searches are distributed across the cluster members. Each search head is responsible for executing a portion of the scheduled searches, and Splunk enforces concurrency limits to prevent resource exhaustion. If a three‑node SHC is skipping a large number of searches, the most likely cause is that the cluster has reached its scheduled search concurrency limit rather than a lack of hardware resources.

This concurrency is controlled by the max_searches_per_cpu setting in limits.conf. By default, Splunk allows a certain number of concurrent searches per CPU core. If the environment has sufficient CPU and memory but searches are still being skipped, increasing this value allows more scheduled searches to run simultaneously. This is the documented and supported method to increase scheduled search capacity.

Why D is Correct
max_searches_per_cpu in limits.conf directly controls how many searches can run concurrently per CPU core.
Increasing this value allows Splunk to take advantage of available hardware resources.
It is the recommended approach when searches are being skipped due to concurrency limits, not due to hardware shortages.
Splunk documentation explicitly references this parameter as the way to tune scheduled search capacity.

Why the other options are not correct

A. Create a job server on the cluster
→ Incorrect Splunk does not use a separate “job server” concept. Search jobs are managed directly by search heads. This option is a distractor and has no basis in Splunk’s architecture.

B. Add another search head to the cluster
→ Incorrect Adding another search head increases distribution capacity but does not directly solve skipped searches caused by concurrency limits. The bottleneck is the per‑CPU search concurrency setting, not the number of search heads. Unless the cluster is under heavy load from ad‑hoc searches, simply adding nodes will not resolve skipped scheduled searches.

C. server.conf captain_is_adhoc_searchhead = true
→ Incorrect This setting controls whether the captain handles ad‑hoc searches. It does not affect scheduled search concurrency. Changing this would not resolve skipped scheduled searches, as the issue lies in concurrency limits defined in limits.conf.

D. Change limits.conf value for max_searches_per_cpu
→ Correct This directly increases scheduled search capacity by allowing more concurrent searches per CPU core. It is the documented method to address skipped searches when hardware resources are sufficient.

References
Splunk Docs – limits.conf
Splunk Docs – Search head cluster overview

Explanation:
In a multi‑site indexer cluster, search heads can be configured with a site attribute to control search site affinity. Search site affinity ensures that searches prefer local site indexers to reduce latency and optimize resource usage.

When you explicitly set site=site0 on all Search Head Cluster (SHC) members, you are telling Splunk that these search heads do not belong to any specific site.

As a result, search site affinity is disabled. Searches will not prefer local site indexers and instead can be distributed across any site in the cluster.

This configuration is sometimes used when you want search heads to operate in a “site‑agnostic” mode, treating all indexers equally regardless of site location.

Why the other options are not correct

B. Sets all members to dynamic captaincy → Incorrect Captaincy in a search head cluster is determined by the Raft consensus algorithm, not by the site attribute. Setting site=site0 has no effect on captain election.

C. Enables multisite search artifact replication → Incorrect Artifact replication is controlled by the SHC itself and cluster replication factors, not by the site setting. site=site0 does not enable replication.

D. Enables automatic search site affinity discovery → Incorrect In fact, the opposite occurs. Setting site=site0 disables site affinity. Automatic discovery is not triggered by this setting.

References
Splunk Docs – Multisite indexer clusters