Free and Premium Splunk SPLK-2002 Dumps Questions Answers

According to the Splunk documentation1, the “Unknown sid” error message means that the search job associated with the link has expired or been deleted. The sid (search ID) is a unique identifier for each search job, and it is used to retrieve the results of the search. If the sid is not found, the search cannot be displayed. The other options are false because:

The users having insufficient permissions would result in a different error message, such as “You do not have permission to view this page” or "You do not have permission to run this search"1.

An add-on needing to be updated would not affect the validity of the sid, unless the add-on changes the search syntax or the data source in a way that makes the search invalid or inaccessible1.

One or more indexers being down would not cause the “Unknown sid” error, as the sid is stored on the search head, not the indexers. However, it could cause other errors, such as “Unable to distribute to peer” or "Search peer has the following message: not enough disk space"1.

In Splunk Enterprise, manually editing the serverclass.conf file on a Deployment Server can lead to the loss of UI edit functionality for server classes in Splunk Web.

The Deployment Server manages app distribution to Universal Forwarders and other deployment clients through server classes, which are defined in serverclass.conf. This file maps deployment clients to specific app configurations and defines filtering rules, restart behaviors, and inclusion/exclusion criteria.

When this configuration file is modified manually (outside of Splunk Web), the syntax, formatting, or logical relationships between entries may not match what Splunk Web expects. As a result, Splunk Web may no longer be able to parse or display those server classes correctly. Once this happens, administrators cannot modify deployment settings through the GUI until the configuration file is corrected or reverted to a valid state.

Other files such as deploymentclient.conf, inputs.conf, and deploymentserver.conf control client settings, data inputs, and core server parameters but do not affect the UI-driven deployment management functionality.

Therefore, Splunk explicitly warns administrators in its Deployment Server documentation to use Splunk Web or the CLI when modifying serverclass.conf, and to avoid manual editing unless fully confident in its syntax.

References (Splunk Enterprise Documentation):

• Deployment Server Overview – Managing Server Classes and App Deployment

• serverclass.conf Reference and Configuration Best Practices

• Splunk Enterprise Admin Manual – GUI Limitations After Manual Edits

• Troubleshooting Deployment Server and Serverclass Configuration Issues

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.

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:

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.

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 replication factor and the search factor are two important settings for a Splunk indexer cluster. The replication factor determines how many copies of each bucket are maintained across the set of peer nodes. The search factor determines how many searchable copies of each bucket are maintained. The default values for both settings are 3, which means that each bucket has three copies, and at least one of them is searchable

The frequency in which a deployment client contacts the deployment server is controlled by the phoneHomeIntervalInSecs attribute in deploymentclient.conf. This attribute specifies how often the deployment client checks in with the deployment server to get updates on the apps and configurations that it should receive. The polling_interval attribute in outputs.conf controls how often the forwarder sends data to the indexer or another forwarder. The polling_interval attribute in deploymentclient.conf and the phoneHomeIntervalInSecs attribute in outputs.conf are not valid Splunk attributes. For more information, see Configure deployment clients and Configure forwarders with outputs.conf in the Splunk documentation.

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

When adding or decommissioning a member from a Search Head Cluster (SHC), the proper order of operations is:

Delete Splunk Enterprise, if it exists.

Install and initialize the instance.

Join the SHC.

This order of operations ensures that the member has a clean and consistent Splunk installation before joining the SHC. Deleting Splunk Enterprise removes any existing configurations and data from the instance. Installing and initializing the instance sets up the Splunk software and the required roles and settings for the SHC. Joining the SHC adds the instance to the cluster and synchronizes the configurations and apps with the other members. The other order of operations are not correct, because they either skip a step or perform the steps in the wrong order.

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

 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

A single-site indexer cluster is a group of Splunk Enterprise instances that index and replicate data across the cluster1. A bucket is a directory that contains indexed data, along with metadata and other information2. A replication factor is the number of copies of each bucket that the cluster maintains1. A search factor is the number of searchable copies of each bucket that the cluster maintains1. A searchable copy is a copy that contains both the raw data and the index files3. A search head is a Splunk Enterprise instance that coordinates the search activities across the peer nodes1.

Option D is the correct answer because it reflects the definitions of replication factor and search factor. The cluster will ensure that there are at least three copies of each bucket, one on each peer node, to satisfy the replication factor of 3. The cluster will also ensure that there are at least two searchable copies of each bucket, one primary and one searchable, to satisfy the search factor of 2. The primary copy is the one that the search head uses to run searches, and the searchable copy is the one that can be promoted to primary if the original primary copy becomes unavailable3.

Option A is incorrect because it confuses the replication factor and the search factor. The cluster will ensure there are at least three copies of each bucket, not two, to meet the replication factor of 3. The cluster will ensure there are at least two copies of searchable metadata, not three, to meet the search factor of 2.

Option B is incorrect because it uses the wrong terms. The cluster will ensure there are at least, not at most, three copies of each bucket, to meet the replication factor of 3. The cluster will ensure there are at least, not at most, two copies of searchable metadata, to meet the search factor of 2.

Option C is incorrect because it has nothing to do with the replication factor or the search factor. The cluster does not limit the number of search heads that can access the bucket at the same time. The search head can search across multiple clusters, and the cluster can serve multiple search heads1.

1: The basics of indexer cluster architecture - Splunk Documentation 2: About buckets - Splunk Documentation 3: Search factor - Splunk Documentation

According to the Splunk documentation1, when migrating an indexer cluster from single-site to multi-site, you must remove the existing single-site attributes from the server.conf file of each peer node. These attributes include replication_factor, search_factor, and cluster_label. You must also restart each peer node after removing the attributes. The other options are false because:

Multi-site policies will apply only to the data created after migration, unless you configure the manager node to convert legacy buckets to multi-site1.

All peer nodes do not need to run the same version of Splunk, as long as they are compatible with the manager node2.

Single-site buckets can be converted to multi-site buckets by changing the constrain_singlesite_buckets setting in the manager node’s server.conf file to "false"1.

The walklex command in Splunk is a specialized administrative search command used to translate and display LISPY (Splunk’s internal representation of search terms). LISPY is the logical search syntax Splunk uses to parse and execute search queries, and examining it helps administrators and developers debug search optimization, field extraction behavior, and index-time search efficiency.

When you run the command | walklex search="your_search_string", Splunk outputs how it tokenizes and interprets that query internally. This is particularly useful for understanding how Splunk’s search language maps to index-time fields and for diagnosing performance issues caused by inefficient search term parsing.

For example:

| walklex search="error OR failure host=server01"

Displays the corresponding LISPY translation used by Splunk’s search subsystem.

Other options are unrelated:

dbinspect provides index bucket metadata.

Monitoring Console shows performance metrics and health status.

Search Job Inspector analyzes search execution phases but doesn’t expose LISPY.

Thus, the correct and Splunk-documented tool for LISPY inspection is the walklex command.

References (Splunk Enterprise Documentation):

• walklex Command Reference – LISPY and Search Debugging

• Understanding Search Language Parsing in Splunk

• Search Internals: How Splunk Interprets Queries

• Splunk Search Performance Troubleshooting Tools

The correct answer is C. Increase the parallellngestionPipelines attribute. This is an option in limits.conf that may provide performance benefits at the forwarding tier, as it allows the forwarder to process multiple data inputs in parallel1. The parallellngestionPipelines attribute specifies the number of pipelines that the forwarder can use to ingest data from different sources1. By increasing this value, the forwarder can improve its throughput and reduce the latency of data delivery1. The other options are not effective options to provide performance benefits at the forwarding tier. Option A, enabling the indexed_realtime_use_by_default attribute, is not recommended, as it enables the forwarder to send data to the indexer as soon as it is received, which may increase the network and CPU load and degrade the performance2. Option B, increasing the maxKBps attribute, is not a good option, as it increases the maximum bandwidth, in kilobytes per second, that the forwarder can use to send data to the indexer3. This may improve the data transfer speed, but it may also saturate the network and cause congestion and packet loss3. Option D, increasing the max_searches_per_cpu attribute, is not relevant, as it only affects the search performance on the indexer or search head, not the forwarding performance on the forwarder4. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Configure parallel ingestion pipelines 2: Configure real-time forwarding 3: Configure forwarder output 4: Configure search performance

When designing the number and size of indexes, the following considerations should be applied:

Expected daily ingest volumes: This is the amount of data that will be ingested and indexed by the Splunk platform per day. This affects the storage capacity, the indexing performance, and the license usage of the Splunk deployment. The number and size of indexes should be planned according to the expected daily ingest volumes, as well as the peak ingest volumes, to ensure that the Splunk deployment can handle the data load and meet the business requirements12.

Data retention time policies: This is the duration for which the data will be stored and searchable by the Splunk platform. This affects the storage capacity, the data availability, and the data compliance of the Splunk deployment. The number and size of indexes should be planned according to the data retention time policies, as well as the data lifecycle, to ensure that the Splunk deployment can retain the data for the desired period and meet the legal or regulatory obligations13.

Access controls: This is the mechanism for granting or restricting access to the data by the Splunk users or roles. This affects the data security, the data privacy, and the data governance of the Splunk deployment. The number and size of indexes should be planned according to the access controls, as well as the data sensitivity, to ensure that the Splunk deployment can protect the data from unauthorized or inappropriate access and meet the ethical or organizational standards14.

Option D is the correct answer because it reflects the most relevant and important considerations for designing the number and size of indexes. Option A is incorrect because the number of concurrent users is not a direct factor for designing the number and size of indexes, but rather a factor for designing the search head capacity and the search head clustering configuration5. Option B is incorrect because the number of installed apps is not a direct factor for designing the number and size of indexes, but rather a factor for designing the app compatibility and the app performance. Option C is incorrect because it omits the expected daily ingest volumes, which is a crucial factor for designing the number and size of indexes.

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, indexer cluster knowledge bundles are the configuration files that the cluster master distributes to the peer nodes as part of the cluster configuration bundle. The knowledge bundles contain the knowledge objects, such as event types, tags, lookups, and so on, that are relevant for indexing and searching the data. The cluster master creates the knowledge bundles by merging the app-name/default and app-name/local directories from the apps that reside on the master node. The cluster master then pushes the knowledge bundles to the peer nodes, where they reside under the $SPLUNK_HOME/var/run directory2. The other options are false because:

Only app-name/local is pushed. This is false because the cluster master pushes both the app-name/default and app-name/local directories, after merging them, to the peer nodes. The app-name/local directory contains the local customizations of the app configuration, while the app-name/default directory contains the default app configuration3.

Only app-name/default is pushed. This is false because the cluster master pushes both the app-name/default and app-name/local directories, after merging them, to the peer nodes. The app-name/default directory contains the default app configuration, while the app-name/local directory contains the local customizations of the app configuration3.

app-name/default and app-name/local are pushed without change. This is false because the cluster master merges the app-name/default and app-name/local directories before pushing them to the peer nodes. This ensures that the peer nodes have the latest and consistent configuration of the apps3.

The following statements about integrating with third-party systems are true: You can use Splunk alerts to provision actions on a third-party system, and you can forward data from Splunk forwarder to a third-party system without indexing it first. Splunk alerts are triggered events that can execute custom actions, such as sending an email, running a script, or calling a webhook. Splunk alerts can be used to integrate with third-party systems, such as ticketing systems, notification services, or automation platforms. For example, you can use Splunk alerts to create a ticket in ServiceNow, send a message to Slack, or trigger a workflow in Ansible. Splunk forwarders are Splunk instances that collect and forward data to other Splunk instances, such as indexers or heavy forwarders. Splunk forwarders can also forward data to third-party systems, such as Hadoop, Kafka, or AWS Kinesis, without indexing it first. This can be useful for sending data to other data processing or storage systems, or for integrating with other analytics or monitoring tools. A Hadoop application cannot search data in Splunk, because Splunk does not provide a native interface for Hadoop applications to access Splunk data. Splunk can search data in the Hadoop File System (HDFS), but only by using the Hadoop Connect app, which is a Splunk app that enables Splunk to index and search data stored in HDFS

When using the props.conf LINE_BREAKER attribute to delimit multi-line events, the SHOULD_LINEMERGE attribute should be set to false. This tells Splunk not to merge events that have been broken by the LINE_BREAKER. Setting the SHOULD_LINEMERGE attribute to true, auto, or none will cause Splunk to ignore the LINE_BREAKER and merge events based on other criteria. For more information, see Configure event line breaking in the Splunk documentation.

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

This is the index that records changes to .conf files as of Splunk 9.0. According to the Splunk documentation1, the _configtracker index tracks the changes made to the configuration files on the Splunk platform, such as the files in the etc directory. The _configtracker index can help monitor and troubleshoot the configuration changes, and identify the source and time of the changes1. The other options are not indexes that record changes to .conf files. Option B, _introspection, is an index that records the performance metrics of the Splunk platform, such as CPU, memory, disk, and network usage2. Option C, _internal, is an index that records the internal logs and events of the Splunk platform, such as splunkd, metrics, and audit logs3. Option D, _audit, is an index that records the audit events of the Splunk platform, such as user authentication, authorization, and activity4. Therefore, option A is the correct answer, and options B, C, and D are incorrect.

1: About the _configtracker index 2: About the _introspection index 3: About the _internal index 4: About the _audit index

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.

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

 The following statements are true regarding Splunk Enterprise performance:

Adding search peers increases the search throughput as search load increases. This is because adding more search peers distributes the search workload across more indexers, which reduces the load on each indexer and improves the search speed and concurrency.

Adding search heads provides additional CPU cores to run more concurrent searches. This is because adding more search heads increases the number of search processes that can run in parallel, which improves the search performance and scalability. The following statements are false regarding Splunk Enterprise performance:

Adding search peers does not increase the maximum size of search results. The maximum size of search results is determined by the maxresultrows setting in the limits.conf file, which is independent of the number of search peers.

Adding RAM to an existing search head does not provide additional search capacity. The search capacity of a search head is determined by the number of CPU cores, not the amount of RAM. Adding RAM to a search head may improve the search performance, but not the search capacity. For more information, see Splunk Enterprise performance in the Splunk documentation.

According to Splunk Enterprise Distributed Clustering documentation, when you add a search head to an indexer cluster, you must configure it to communicate with the Cluster Manager (previously known as Master Node). The proper way to initialize this connection is by editing the cluster configuration using the splunk edit cluster-config command.

The correct syntax for a search head is:

splunk edit cluster-config -mode searchhead -manager_uri :8089 -secret

Here:

-mode searchhead specifies that this node will function as a search head that participates in distributed search across the indexer cluster.

-manager_uri provides the management URI of the cluster manager.

-secret defines the shared secret key used for secure communication between the manager and cluster members.

Once this configuration is applied, the search head must be restarted for the changes to take effect.

Using -mode peer (Option B) is for indexers joining the cluster, not search heads. The add cluster-manager command (Options C and D) is not a valid Splunk command.

References (Splunk Enterprise Documentation):

• Configure the Search Head for an Indexer Cluster

• Indexer Clustering: Configure the Cluster Manager, Peer, and Search Head Nodes

• Splunk Enterprise Admin Manual: splunk edit cluster-config Command Reference

A lookup table is a file that contains a list of values that can be used to enrich or modify the data during search time1. Lookup tables can be stored in CSV files or in the KV Store1. Troubleshooting lookup tables involves identifying and resolving issues that prevent the lookup tables from being accessed, updated, or applied correctly by the Splunk searches. Some of the tools and methods that can help with troubleshooting lookup tables are:

web_access.log: This is a file that contains information about the HTTP requests and responses that occur between the Splunk web server and the clients2. This file can help troubleshoot issues related to lookup table permissions, availability, and errors, such as 404 Not Found, 403 Forbidden, or 500 Internal Server Error34.

btool output: This is a command-line tool that displays the effective configuration settings for a given Splunk component, such as inputs, outputs, indexes, props, and so on5. This tool can help troubleshoot issues related to lookup table definitions, locations, and precedence, as well as identify the source of a configuration setting6.

search.log: This is a file that contains detailed information about the execution of a search, such as the search pipeline, the search commands, the search results, the search errors, and the search performance. This file can help troubleshoot issues related to lookup table commands, arguments, fields, and outputs, such as lookup, inputlookup, outputlookup, lookup_editor, and so on .

Option B is the correct answer because web_access.log is the best place to start troubleshooting lookup table issues, as it can provide the most relevant and immediate information about the lookup table access and status. Option A is incorrect because btool output is not a log file, but a command-line tool. Option C is incorrect because health.log is a file that contains information about the health of the Splunk components, such as the indexer cluster, the search head cluster, the license master, and the deployment server. This file can help troubleshoot issues related to Splunk deployment health, but not necessarily related to lookup tables. Option D is incorrect because configuration_change.log is a file that contains information about the changes made to the Splunk configuration files, such as the user, the time, the file, and the action. This file can help troubleshoot issues related to Splunk configuration changes, but not necessarily related to lookup tables.

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

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

mode = manager

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

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

mode = peer

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

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

mode = searchhead

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

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

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

References (Splunk Enterprise Documentation):

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

• server.conf Reference – [clustering] Stanza Attributes

• Distributed Search and Cluster Node Role Configuration

• Splunk Enterprise Admin Manual – Cluster Deployment Architecture

According to the Splunk documentation1, multi-site clustering is an indexer cluster that spans multiple physical sites, such as data centers. Each site has its own set of peer nodes and search heads. Each site also obeys site-specific replication and search factor rules. The use cases that are made possible by multi-site clustering are:

Greatly reduce WAN traffic by preferentially searching assigned site (search affinity). This means that if you configure each site so that it has both a search head and a full set of searchable data, the search head on each site will limit its searches to local peer nodes. This eliminates any need, under normal conditions, for search heads to access data on other sites, greatly reducing network traffic between sites2.

Seamlessly route searches to a redundant site in case of a site failure. This means that by storing copies of your data at multiple locations, you maintain access to the data if a disaster strikes at one location. Multisite clusters provide site failover capability. If a site goes down, indexing and searching can continue on the remaining sites, without interruption or loss of data2.

The other options are false because:

Use blockchain technology to audit search activity from geographically dispersed data centers. This is not a use case of multi-site clustering, as Splunk does not use blockchain technology to audit search activity. Splunk uses its own internal logs and metrics to monitor and audit search activity3.

Enable a forwarder to send data to multiple indexers. This is not a use case of multi-site clustering, as forwarders can send data to multiple indexers regardless of whether they are in a single-site or multi-site cluster. This is a basic feature of forwarders that allows load balancing and high availability of data ingestion4.

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 Indexer Clustering Administration Guide, configuration bundles pushed from the Cluster Manager (Master Node) overwrite the contents of the $SPLUNK_HOME/etc/slave-apps/ directory on each search peer (indexer). However, Splunk provides a special persistent location — the _cluster app’s local directory — for files that must survive bundle redeployments.

Specifically, any configuration files placed in:

$SPLUNK_HOME/etc/slave-apps/_cluster/local/

will persist after future bundle pushes because this directory is excluded from the automatic overwrite process.

This is particularly useful for maintaining local overrides or custom configurations that should not be replaced by the Cluster Manager, such as environment-specific inputs, temporary test settings, or monitoring configurations unique to that peer.

Other directories under slave-apps are overwritten each time a configuration bundle is pushed, ensuring consistency across the cluster. Likewise, master-apps exists only on the Cluster Manager and is used for deployment, not persistence.

Thus, the _cluster/local folder is the only safe, Splunk-documented location for configurations that need to survive bundle redeployment.

References (Splunk Enterprise Documentation):

• Indexer Clustering: How Configuration Bundles Work

• Maintaining Local Configurations on Clustered Indexers

• slave-apps and _cluster App Structure and Behavior

• Splunk Enterprise Admin Manual – Cluster Configuration Management Best Practices

The Splunk Enterprise Security and Encryption documentation specifies that the primary mechanism for securing data in motion within a Splunk environment is to enable TLS/SSL encryption between forwarders and indexers. This ensures that log data transmitted from Universal Forwarders or Heavy Forwarders to Indexers is fully encrypted and protected from interception or tampering.

The correct configuration involves setting up signed SSL certificates on both forwarders and indexers:

On the forwarder, TLS settings are defined in outputs.conf, specifying parameters like sslCertPath, sslPassword, and sslRootCAPath.

On the indexer, TLS is enabled in inputs.conf and server.conf using the same shared CA for validation.

Splunk’s documentation explicitly states that this configuration protects data-in-transit between the collection (forwarder) and indexing (storage) tiers — which is the critical link where sensitive log data is most vulnerable.

Other communication channels (e.g., deployment server to clients or browser to Splunk Web) can also use encryption but do not secure the ingestion pipeline that handles the indexed data stream. Therefore, TLS should be implemented between Splunk forwarders and indexers.

References (Splunk Enterprise Documentation):

• Securing Data in Transit with SSL/TLS

• Configure Forwarder-to-Indexer Encryption Using SSL Certificates

• Server and Forwarder Authentication Setup Guide

• Splunk Enterprise Admin Manual – Security and Encryption Best Practices

 The following is a good practice for a search head cluster deployer: The deployer must be used to distribute non-replicable configurations to search head cluster members. Non-replicable configurations are the configurations that are not replicated by the search factor, such as the apps and the server.conf settings. The deployer is the Splunk server role that distributes these configurations to the search head cluster members, ensuring that they have the same configuration. The deployer does not only distribute configurations to search head cluster members when they “phone home”, as this would cause configuration inconsistencies and delays. The deployer does not distribute configurations to search head cluster members to be valid configurations, as this implies that the configurations are invalid without the deployer. The deployer does not only distribute configurations to search head cluster members with splunk apply shcluster-bundle, as this would require manual intervention by the administrator. For more information, see Use the deployer to distribute apps and configuration updates in the Splunk documentation.

Adding more search peers and making sure forwarders distribute data evenly across all indexers will provide the most search performance improvement when the distributed deployment is approaching its capacity. Adding more search peers will increase the search concurrency and reduce the load on each indexer. Distributing data evenly across all indexers will ensure that the search workload is balanced and no indexer becomes a bottleneck. Replacing the indexer storage to SSD will improve the search performance, but it is a costly and time-consuming option. Adding more search heads will not improve the search performance if the indexers are the bottleneck. Rescheduling slow searches to run during an off-peak time will reduce the search contention, but it will not improve the search performance for each individual search. For more information, see [Scale your indexer cluster] and [Distribute data across your indexers] in the Splunk documentation.

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.

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.

When installing Enterprise Security on a Search Head Cluster (SHC), the following steps should be done: Install Enterprise Security on the deployer, and use the deployer to deploy Enterprise Security to the cluster members. Enterprise Security is a premium app that provides security analytics and monitoring capabilities for Splunk. Enterprise Security can be installed on a SHC by using the deployer, which is a standalone instance that distributes apps and other configurations to the SHC members. Enterprise Security should be installed on the deployer first, and then deployed to the cluster members using the splunk apply shcluster-bundle command. Enterprise Security should not be installed on a staging instance, because a staging instance is not part of the SHC deployment process. Enterprise Security configurations should not be copied to the deployer, because they are already included in the Enterprise Security app package.

Splunk Enterprise performs a CRC check against the first and last 256 bytes of a file by default, as stated in the inputs.conf specification. This is controlled by the initCrcLength parameter, which can be changed if needed. The CRC check helps Splunk Enterprise to avoid re-indexing the same file twice, even if it is renamed or rotated, as long as the content does not change. However, this also means that Splunk Enterprise might miss some files that have the same CRC but different content, especially if they have identical headers. To avoid this, the crcSalt parameter can be used to add some extra information to the CRC calculation, such as the full file path or a custom string. This ensures that each file has a unique CRC and is indexed by Splunk Enterprise. You can read more about crcSalt and initCrcLength in the How log file rotation is handled documentation.

 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.

 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.

The master node is the Splunk server role that regulates the functioning of the indexer cluster. The master node coordinates the activities of the peer nodes, such as data replication, data searchability, and data recovery. The master node also manages the cluster configuration bundle and distributes it to the peer nodes. The indexer is the Splunk server role that indexes the incoming data and makes it searchable. The deployer is the Splunk server role that distributes apps and configuration updates to the search head cluster members. The monitoring console is the Splunk server role that monitors the health and performance of the Splunk deployment. For more information, see About indexer clusters and index replication in the Splunk documentation.

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

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

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s documentation on summary indexing and data-model acceleration clarifies that summary data is stored as additional indexed data on the indexers. Summary indexing produces new events—aggregations, rollups, scheduled search outputs—and stores them in summary indexes. Splunk explains that these summaries accumulate over time and require additional bucket storage, retention considerations, and sizing adjustments.

The documentation for accelerated data models further confirms that acceleration summaries are stored alongside raw data on indexers, increasing disk usage proportional to the acceleration workload. This makes summary indexing the only listed feature that strictly increases indexer storage demand.

In contrast, Search Head Clustering replicates configuration and knowledge objects across search heads—not on indexers. Indexer Discovery affects forwarder behavior, not storage. Indexer Acknowledgement controls data-delivery guarantees but does not create extra indexed content.

Therefore, only Index Summarization (summary indexing) directly increases indexer storage requirements.

Splunk provides various methods to change the internal logging levels in a Splunk environment to troubleshoot an issue. All of the options are valid ways to do so. Option A is correct because the Monitoring Console (MC) allows the administrator to view and modify the logging levels of various Splunk components through a graphical interface. Option B is correct because the Splunk command line provides the splunk set log-level command to change the logging levels of specific components or categories. Option C is correct because the Splunk Web provides the Settings > Server settings > Server logging page to change the logging levels of various components through a web interface. Option D is correct because the log-local.cfg file allows the administrator to manually edit the logging levels of various components by overriding the default settings in the log.cfg file123

1:

All of the forwarders would still be phoning home to the old deployment server, because the forwarders are configured to use the old deployment server in $SPLUNK_HOME/etc/system/local. This is the local configuration directory that contains the settings that override the default settings in $SPLUNK_HOME/etc/system/default. The deploymentclient.conf file in the local directory specifies the targetUri of the deployment server that the forwarder contacts for configuration updates and apps. If the forwarders have the old deployment server’s targetUri in the local directory, they will ignore the updated deploymentclient.conf file that was deployed by the old deployment server, because the local settings have higher precedence than the deployed settings. To fix this issue, the forwarders should either remove the deploymentclient.conf file from the local directory, or update it with the new deployment server’s targetUri. Option C is the correct answer. Option A is incorrect because a version mismatch between the forwarders and the new deployment server would not prevent the forwarders from phoning home to the new deployment server, as long as they are compatible versions. Option B is incorrect because the new deployment server is configured and running, and there is no indication that it is not accepting connections from the forwarders. Option D is incorrect because the pass4SymmKey is the shared secret key that the deployment server and the forwarders use to authenticate each other. It does not affect the forwarders’ ability to phone home to the new deployment server, as long as it is the same on both sides12

1:

In the deployment planning process, a person should identify who gets to see network data in the data policy definition step. This step involves defining the data access policies and permissions for different users and roles in Splunk. The deployment schedule step involves defining the timeline and milestones for the deployment project. The topology diagramming step involves creating a visual representation of the Splunk architecture and components. The data source inventory step involves identifying and documenting the data sources and types that will be ingested by Splunk

 A Technical Add-On (TA) is a Splunk app that contains configurations for data collection, parsing, and enrichment. It can also enable event data for a data model, which is useful for creating dashboards and reports. Therefore, before installing a TA, it is important to identify the number of scheduled or real-time searches that will use the data model, and to validate if the TA enables event data for a data model. The number of forwarders that the TA can support is not relevant, as the TA is installed on the indexer or search head, not on the forwarder. The installation location of the TA depends on the type of data and the use case, so it is not a fixed requirement

 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.

 A Splunk job is a search process that is kicked off via a report, an alert, or a user action. A Splunk job is a child OS process manifested from the splunkd process, which is the main Splunk daemon. A Splunk job is subjected to some usage quota, such as memory, CPU, and disk space, which can be configured in the limits.conf file. A Splunk job is not a user-defined Splunk capability, as it is a core feature of the Splunk platform.

 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

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

Migration from single-site to multisite index replication only affects new data, not existing data. Multisite policies apply to new data only, meaning that data that is ingested after the migration will follow the multisite replication and search factors. Existing data, or data that was ingested before the migration, will retain the single-site policies, unless they are manually converted to multisite buckets. Single-site buckets do not instantly receive the multisite policies, nor do they automatically convert to multisite buckets. Multisite total values can exceed any single-site factors, as long as they do not exceed the number of peer nodes in the cluster. A master node is not required at each site, only one master node is needed for the entire cluster

 ITSI can impact the planning of a Splunk deployment depending on the Key Performance Indicators (KPIs) that are being tracked. KPIs are metrics that measure the health and performance of IT services and business processes. ITSI collects, analyzes, and displays KPI data from various data sources in Splunk. Depending on the number, frequency, and complexity of the KPIs, additional infrastructure may be needed to support the data ingestion, processing, and visualization. ITSI does not require a dedicated deployment server, nor does it affect the number of users using ITSI. ITSI in a Splunk deployment does require additional hardware resources, such as CPU, memory, and disk space, to run the ITSI components and apps

 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.

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

The 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