Free Splunk SPLK-2002 Practice Exam with Questions & Answers | Set: 2

A deployment plan should include business continuity and disaster recovery plans, current logging details and data source inventory, and current and future topology diagrams of the IT environment. These elements are essential for planning, designing, and implementing a Splunk deployment that meets the business and technical requirements. A comprehensive list of stakeholders, either direct or indirect, is not part of the deployment plan, but rather part of the project charter. For more information, see Deployment planning in the Splunk documentation.

According to the Splunk documentation1, metrics.log is a file that contains various metrics data for reviewing product behavior, such as pipeline, queue, thruput, and tcpout_connections. Metrics.log is stored in the _internal index by default2, which is a special index that contains internal logs and metrics for Splunk Enterprise. The other options are false because:

main is the default index for user data, not internal data3.

_telemetry is an index that contains data collected by the Splunk Telemetry feature, which sends anonymous usage and performance data to Splunk4.

_introspection is an index that contains data collected by the Splunk Monitoring Console, which monitors the health and performance of Splunk components.

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

According to the Splunk SmartStore Architecture Guide, the primary benefit of SmartStore is the separation of storage from compute resources within an indexer cluster. SmartStore enables Splunk to decouple indexer storage (data at rest) from the compute layer (indexers that perform searches and indexing).

With SmartStore, active (hot/warm) data remains on local disk for fast access, while older, less frequently searched (remote) data is stored in an external object storage system such as Amazon S3, Google Cloud Storage, or on-premises S3-compatible storage. This separation reduces the storage footprint on indexers, allowing organizations to scale compute and storage independently.

This architecture improves cost efficiency and scalability by:

Lowering on-premises storage costs using object storage for retention.

Enabling dynamic scaling of indexers without impacting total data availability.

Reducing replication overhead since SmartStore manages data objects efficiently.

SmartStore does not affect replication or search factors (Option A), does not handle Knowledge Object replication (Option C), and the Cluster Manager is still required (Option D) to coordinate cluster activities.

References (Splunk Enterprise Documentation):

• SmartStore Overview and Architecture Guide

• SmartStore Deployment and Configuration Manual

• Managing Storage and Compute Independence in Indexer Clusters

• Splunk Enterprise Capacity Planning – SmartStore Sizing Guidelines

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

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 license_usage.log file is generated and maintained on the License Manager node in a Splunk deployment. This log provides detailed statistics about daily license consumption, including data volume indexed per pool, index, sourcetype, source, and host.

In a distributed or clustered environment (with both search head and indexer clusters), the License Manager acts as the central authority that collects license usage information from all indexers and consolidates it into this log. The License Manager receives periodic reports from each license peer (indexer) and records them in:

$SPLUNK_HOME/var/log/splunk/license_usage.log

The log is automatically indexed into the _internal index with sourcetype=splunkd and can be queried using searches such as:

index=_internal source=*license_usage.log* type="RolloverSummary"

Other components like the Cluster Manager, SHC Deployer, or individual indexers do not store the full consolidated license usage data — they only send summarized reports to the License Manager.

Therefore, the License Manager is the definitive and Splunk-documented location for retrieving and analyzing license_usage.log data across a distributed deployment.

References (Splunk Enterprise Documentation):

• Managing Licenses in a Distributed Environment

• license_usage.log Reference and Structure

• Monitoring License Consumption Using the License Manager

• Splunk Enterprise Admin Manual – License Reporting and Troubleshooting

The following statements describe a search head cluster captain:

Is the job scheduler for the entire search head cluster. The captain is responsible for scheduling and dispatching the searches that run on the search head cluster, as well as coordinating the search results from the search peers. The captain also ensures that the scheduled searches are balanced across the search head cluster members and that the search concurrency limits are enforced.

Replicates the search head cluster’s knowledge bundle to the search peers. The captain is responsible for creating and distributing the knowledge bundle to the search peers, which contains the knowledge objects that are required for the searches. The captain also ensures that the knowledge bundle is consistent and up-to-date across the search head cluster and the search peers. The following statements do not describe a search head cluster captain:

Manages alert action suppressions (throttling). Alert action suppressions are the settings that prevent an alert from triggering too frequently or too many times. These settings are managed by the search head that runs the alert, not by the captain. The captain does not have any special role in managing alert action suppressions.

Synchronizes the member list with the KV store primary. The member list is the list of search head cluster members that are active and available. The KV store primary is the search head cluster member that is responsible for replicating the KV store data to the other members. These roles are not related to the captain, and the captain does not synchronize them. The member list and the KV store primary are determined by the RAFT consensus algorithm, which is independent of the captain election. For more information, see [About the captain and the captain election] and [About KV store and search head clusters] in the Splunk documentation.

Increasing the search factor in the cluster will best address the requirement of high availability for searchable data. The search factor determines how many copies of searchable data are maintained by the cluster. A higher search factor means that more indexers can serve the data in case of a failure or a maintenance event. Increasing the replication factor will improve the availability of raw data, but not searchable data. Increasing the number of search heads or CPUs on the indexers will improve the search performance, but not the availability of searchable data. For more information, see Replication factor and search factor in the Splunk documentation.

The curl https://serverhost:8089/services/admin/inputstatus/TailingProcessor:FileStatus command is used to check the status of the tailed files when troubleshooting monitor inputs. Monitor inputs are inputs that monitor files or directories for new data and send the data to Splunk for indexing. The TailingProcessor:FileStatus endpoint returns information about the files that are being monitored by the Tailing Processor, such as the file name, path, size, position, and status. The splunk cmd btool inputs list | tail command is used to list the inputs configurations from the inputs.conf file and pipe the output to the tail command. The splunk cmd btool check inputs layer command is used to check the inputs configurations for syntax errors and layering. The curl https://serverhost:8089/services/admin/inputstatus/TailingProcessor:Tailstatus command does not exist, and it is not a valid endpoint.