Splunk SPLK-2002 Practice Exams
Last updated on Oct 07,2026- Exam Code: SPLK-2002
- Exam Name: Splunk Enterprise Certified Architect Exam
- Certification Provider: Splunk
- Latest update: Oct 07,2026
Which part of the deployment plan is vital prior to installing Splunk indexer clusters and search head clusters?
- A . Data source inventory.
- B . Data policy definitions.
- C . Splunk deployment topology.
- D . Education and training plans.
C
Explanation:
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.
Which of the following commands is used to clear the KV store?
- A . splunk clean kvstore
- B . splunk clear kvstore
- C . splunk delete kvstore
- D . splunk reinitialize kvstore
A
Explanation:
The splunk clean kvstore command is used to clear the KV store. This command will delete all the collections and documents in the KV store and reset it to an empty state. This command can be useful for troubleshooting KV store issues or resetting the KV store data. The splunk clear kvstore, splunk delete kvstore, and splunk reinitialize kvstore commands are not valid Splunk commands. For more information, see Use the CLI to manage the KV store in the Splunk documentation.
What information is needed about the current environment before deploying Splunk? (select all that apply)
- A . List of vendors for network devices.
- B . Overall goals for the deployment.
- C . Key users.
- D . Data sources.
BCD
Explanation:
Before deploying Splunk, it is important to gather some information about the current environment, such as:
Overall goals for the deployment: This includes the business objectives, the use cases, the expected outcomes, and the success criteria for the Splunk deployment. This information helps to define the scope, the requirements, the design, and the validation of the Splunk solution1.
Key users: This includes the roles, the responsibilities, the expectations, and the needs of the different types of users who will interact with the Splunk deployment, such as administrators, analysts, developers, and end users. This information helps to determine the user access, the user experience, the user training, and the user feedback for the Splunk solution1.
Data sources: This includes the types, the formats, the volumes, the locations, and the characteristics of the data that will be ingested, indexed, and searched by the Splunk deployment. This information helps to estimate the data throughput, the data retention, the data quality, and the data analysis for the Splunk solution1.
Option B, C, and D are the correct answers because they reflect the essential information that is needed before deploying Splunk.
Option A is incorrect because the list of vendors for network devices is not a relevant information for the Splunk deployment. The network devices may be part of the data sources, but the vendors are not important for the Splunk solution.
Reference: 1: Splunk Validated Architectures
Which of the following statements describe licensing in a clustered Splunk deployment? (Select all that apply.)
- A . Free licenses do not support clustering.
- B . Replicated data does not count against licensing.
- C . Each cluster member requires its own clustering license.
- D . Cluster members must share the same license pool and license master.
A, B
Explanation:
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
Which Splunk internal field can confirm duplicate event issues from failed file monitoring?
- A . _time
- B . _indextime
- C . _index_latest
- D . latest
B
Explanation:
According to the Splunk documentation1, the _indextime field is the time when Splunk indexed the
event. This field can be used to confirm duplicate event issues from failed file monitoring, as it can show you when each duplicate event was indexed and if they have different _indextime values. You can use the Search Job Inspector to inspect the search job that returns the duplicate events and check the _indextime field for each event2. The other options are false because:
The _time field is the time extracted from the event data, not the time when Splunk indexed the event. This field may not reflect the actual indexing time, especially if the event data has a different time zone or format than the Splunk server1.
The _index_latest field is not a valid Splunk internal field, as it does not exist in the Splunk documentation or the Splunk data model3.
The latest field is a field that represents the latest time bound of a search, not the time when Splunk indexed the event. This field is used to specify the time range of a search, along with the earliest field4.
A search head has successfully joined a single site indexer cluster.
Which command is used to configure the same search head to join another indexer cluster?
- A . splunk add cluster-config
- B . splunk add cluster-master
- C . splunk edit cluster-config
- D . splunk edit cluster-master
B
Explanation:
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.
A Splunk deployment is being architected and the customer will be using Splunk Enterprise Security (ES) and Splunk IT Service Intelligence (ITSI). Through data onboarding and sizing, it is determined that over 200 discrete KPIs will be tracked by ITSI and 1TB of data per day by ES.
What topology ensures a scalable and performant deployment?
- A . Two search heads, one for ITSI and one for ES.
- B . Two search head clusters, one for ITSI and one for ES.
- C . One search head cluster with both ITSI and ES installed.
- D . One search head with both ITSI and ES installed.
B
Explanation:
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
Which of the following options can improve reliability of syslog delivery to Splunk? (Select all that apply.)
- A . Use TCP syslog.
- B . Configure UDP inputs on each Splunk indexer to receive data directly.
- C . Use a network load balancer to direct syslog traffic to active backend syslog listeners.
- D . Use one or more syslog servers to persist data with a Universal Forwarder to send the data to Splunk indexers.
A, D
Explanation:
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.
Which of the following statements describe search head clustering? (Select all that apply.)
- A . A deployer is required.
- B . At least three search heads are needed.
- C . Search heads must meet the high-performance reference server requirements.
- D . The deployer must have sufficient CPU and network resources to process service requests and push configurations.
A, B, D
Explanation:
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.
Why should intermediate forwarders be avoided when possible?
- A . To minimize license usage and cost.
- B . To decrease mean time between failures.
- C . Because intermediate forwarders cannot be managed by a deployment server.
- D . To eliminate potential performance bottlenecks.
D
Explanation:
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.”