SPLK-2002 Practice Exam Questions with Answers Splunk Enterprise Certified Architect Certification

The guidance Splunk gives for estimating size on for syslog data is 50% of original data size. This divides between files in the index as follows: rawdata is 15%, tsidx is 35%. The rawdata is the compressed version of the original data, which typically takes about 15% of the original data size. The tsidx is the index file that contains the time-series metadata and the inverted index, which typically takes about 35% of the original data size. The total size of the rawdata and the tsidx is about 50% of the original data size. For more information, see [Estimate your storage requirements] in the Splunk documentation.

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.

References:

1: Splunk Validated Architectures

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.

The correct Splunk deployment to have the recommended minimum components for a high-availability search head cluster is 3 search heads, 1 deployer, 3 indexers. This configuration ensures that the search head cluster has at least three members, which is the minimum number required for a quorum and failover1. The deployer is a separate instance that manages the configuration updates for the search head cluster2. The indexers are the nodes that store and index the data, and having at least three of them provides redundancy and load balancing3. The other options are not recommended, as they either have less than three search heads or less than three indexers, which reduces the availability and reliability of the cluster. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: About search head clusters 2: Use the deployer to distribute apps and configuration updates 3: About indexer clusters and index replication

The splunk_instrumentation.log file is the least helpful in troubleshooting a crash, because it contains information about the Splunk Instrumentation feature, which collects and sends usage data to Splunk Inc. for product improvement purposes. This file does not contain any information about the Splunk processes, errors, or crashes. The other options are more helpful in troubleshooting a crash, because they contain relevant information about the Splunk daemon, the standard error output, and the crash report12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunk_instrumentation.log  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunkd_stderr.log

The splunk offline --enforce-counts command will permanently decommission a peer node operating in an indexer cluster. This command will remove the peer node from the cluster and delete its data. This command should be used when the peer node is no longer needed or is being replaced by another node. The splunk stop -f command will stop the Splunk service on the peer node, but it will not decommission it from the cluster. The splunk offline -f command will take the peer node offline, but it will not delete its data or enforce the replication and search factors. The splunk decommission --enforce-counts command is not a valid Splunk command. For more information, see Remove a peer node from an indexer cluster in the Splunk documentation.

 The splunk diag --exclude command is a way to exclude search artifacts when creating a diag. A diag is a diagnostic snapshot of a Splunk instance that contains various logs, configurations, and other information. Search artifacts are temporary files that are generated by search jobs and stored in the dispatch directory. Search artifacts can be excluded from the diag by using the --exclude option and specifying the dispatch directory. The splunk diag --debug --refresh command is a way to create a diag with debug logging enabled and refresh the diag if it already exists. The splunk diag --disable=dispatch command is not a valid command, because the --disable option does not exist. The splunk diag --filter-searchstrings command is a way to filter out sensitive information from the search strings in the diag

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

• Use case checklist. A use case checklist is a document that lists the use cases that the deployment will support, along with the data sources, the data volume, the data retention, the data model, the dashboards, the reports, the alerts, and the roles and permissions for each use case. A use case checklist helps to define the scope and the functionality of the deployment, and to identify the dependencies and the requirements for each use case1
• Inventory data sources. An inventory of data sources is a document that lists the data sources that the deployment will ingest, along with the data type, the data format, the data location, the data collection method, the data volume, the data frequency, and the data owner for each data source. An inventory of data sources helps to determine the data ingestion strategy, the data parsing and enrichment, the data storage and retention, and the data security and compliance for the deployment1
• Review network topology. A review of network topology is a process that examines the network infrastructure and the network connectivity of the deployment, along with the network bandwidth, the network latency, the network security, and the network monitoring for the deployment. A review of network topology helps to optimize the network performance and reliability, and to identify the network risks and mitigations for the deployment1

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

The Splunk server name of the member can typically be determined by the serverName attribute in the server.conf file, which is not explicitly shown in the provided snippet. However, based on the provided configuration snippet, we can infer that this search head cluster member is configured to communicate with a cluster master (master_uri) located at node1 and a management node (mgmt_uri) located at node3. The serverName is not the same as the master_uri or mgmt_uri; these URIs indicate the location of the master and management nodes that this member interacts with.

Since the serverName is not provided in the snippet, one would typically look for a setting under the [general] stanza in server.conf. However, given the options and the common naming conventions in a Splunk environment, node3 would be a reasonable guess for the server name of this member, since it is indicated as the management URI within the [shclustering] stanza, which suggests it might be the name or address of the server in question.

For accurate identification, you would need to access the full server.conf file or the Splunk Web on the search head cluster member and look under Settings > Server settings > General settings to find the actual serverName. Reference for these details would be found in the Splunk documentation regarding the configuration files, particularly server.conf.

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.

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

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.”

Index files (. tsidx files) are the main components of an index that store the raw data and the inverted index of terms. They take the most space in an index, especially if the raw data has many unique terms that increase the size of the inverted index. Bloom filters, source metadata, and sourcetype metadata are much smaller in comparison and do not depend on the number of unique terms in the raw data.

References:

• How the indexer stores indexes
• Splunk Enterprise Certified Architect Study Guide, page 17

Three peers can fail. This is the maximum number of search peers that can fail before data becomes unsearchable in the indexer cluster with the given characteristics. The searchability of the data depends on the Search Factor, which is the number of searchable copies of each bucket that the cluster maintains across the set of peer nodes1. In this case, the Search Factor is 3, which means that each bucket has three searchable copies distributed among the 10 search peers. If three or fewer search peers fail, the cluster can still serve the data from the remaining searchable copies. However, if four or more search peers fail, the cluster may lose some searchable copies and the data may become unsearchable. The other options are not correct, as they either underestimate or overestimate the number of search peers that can fail before data becomes unsearchable. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: Configure the search factor

 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

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

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

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

The following security option must be explicitly configured, as it is not enabled by default:

• Certificate authentication between forwarders and indexers. This option allows the forwarders and indexers to verify each other’s identity using SSL certificates, which prevents unauthorized data transmission or spoofing attacks. This option is not enabled by default, as it requires the administrator to generate and distribute the certificates for the forwarders and indexers. For more information, see [Secure the communication between forwarders and indexers] in the Splunk documentation. The following security options are enabled by default:
• Data encryption between Splunk Web and splunkd. This option encrypts the communication between the Splunk Web interface and the splunkd daemon using SSL, which prevents data interception or tampering. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [About securing Splunk Enterprise with SSL] in the Splunk documentation.
• Certificate authentication between Splunk Web and search head. This option allows the Splunk Web interface and the search head to verify each other’s identity using SSL certificates, which prevents unauthorized access or spoofing attacks. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [About securing Splunk Enterprise with SSL] in the Splunk documentation.
• Data encryption for distributed search between search heads and indexers. This option encrypts the communication between the search heads and the indexers using SSL, which prevents data interception or tampering. This option is enabled by default, as Splunk provides a self-signed certificate for this purpose. For more information, see [Secure your distributed search environment] in the Splunk documentation.

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.

References:

1: About lookups - Splunk Documentation 2: web_access.log - Splunk Documentation 3: Troubleshoot lookups to the Splunk Enterprise KV Store 4: Troubleshoot lookups in Splunk Enterprise Security - Splunk Documentation 5: Use btool to troubleshoot configurations - Splunk Documentation 6: Troubleshoot configuration issues - Splunk Documentation : Use the search.log file - Splunk Documentation : Troubleshoot search-time field extraction - Splunk Documentation : [Troubleshoot lookups - Splunk Documentation] : [health.log - Splunk Documentation] : [configuration_change.log - Splunk Documentation]

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

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.

 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.

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 deployer sends apps to the search head cluster members by default to the path etc/shcluster/<app-name>/default. The deployer is a Splunk component that distributes apps and configurations to members of a search head cluster.

Splunk's documentation recommends placing the configuration bundle in the $SPLUNK_HOME/etc/shcluster/apps directory on the deployer, which then gets distributed to the search head cluster members. However, it should be noted that within each app's directory, configurations can be under default or local subdirectories, with local taking precedence over default for configurations. The reference to etc/shcluster/<app-name>/default is not a standard directory structure and might be a misunderstanding. The correct path where the deployer pushes configuration bundles is $SPLUNK_HOME/etc/shcluster/apps

The site_mappings attribute should be added to the master node’s server.conf file when decommissioning a site in an indexer cluster. The site_mappings attribute is used to specify how the master node should reassign the buckets from the decommissioned site to the remaining sites. The site_mappings attribute is a comma-separated list of site pairs, where the first site is the decommissioned site and the second site is the destination site. For example, site_mappings = site1:site2,site3:site4 means that the buckets from site1 will be moved to site2, and the buckets from site3 will be moved to site4. The available_sites attribute is used to specify which sites are currently available in the cluster, and it is automatically updated by the master node. The site_search_factor and site_replication_factor attributes are used to specify the number of searchable and replicated copies of each bucket for each site, and they are not affected by the decommissioning process

A multisite indexer cluster is a type of indexer cluster that spans multiple geographic locations or sites. A multisite indexer cluster requires only one cluster manager, also known as the master node, for the entire cluster. The cluster manager is responsible for coordinating the replication and search activities among the peer nodes across all sites. The cluster manager can reside in any site, but it must be accessible by all peer nodes and search heads in the cluster. Option C is the correct answer. Option A is incorrect because having two cluster managers for the entire cluster would introduce redundancy and complexity. Option B is incorrect because having one cluster manager for each site would create separate clusters, not a multisite cluster. Option D is incorrect because having two cluster managers for each site would be unnecessary and inefficient12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Indexer/Multisiteoverview  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Indexer/Clustermanageroverview

The system local directory has the highest precedence among the following directories that contain Splunk configuration files of the same name within different apps. Splunk configuration files are stored in various directories under the SPLUNK_HOME/etc directory. The precedence of these directories determines which configuration file settings take effect when there are conflicts or overlaps. The system local directory, which is located at SPLUNK_HOME/etc/system/local, has the highest precedence among all directories, because it contains the system-level configurations that are specific to the instance. The system default directory, which is located at SPLUNK_HOME/etc/system/default, has the lowest precedence among all directories, because it contains the system-level configurations that are provided by Splunk and should not be modified. The app local directories, which are located at SPLUNK_HOME/etc/apps/APP_NAME/local, have a higher precedence than the app default directories, which are located at SPLUNK_HOME/etc/apps/APP_NAME/default, because the local directories contain the app-level configurations that are specific to the instance, while the default directories contain the app-level configurations that are provided by the app and should not be modified. The app local and default directories have different precedences depending on the ASCII order of the app names, with the app names that come later in the ASCII order having higher precedences.

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 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

 To reduce the captain’s work load in a search head cluster, the setting that will prevent scheduled searches from running on the captain is captain_is_adhoc_searchhead = true (on the current captain). This setting will designate the current captain as an ad hoc search head, which means that it will not run any scheduled searches, but only ad hoc searches initiated by users. This will reduce the captain’s work load and improve the search head cluster performance. The adhoc_searchhead = true (on all members) setting will designate all search head cluster members as ad hoc search heads, which means that none of them will run any scheduled searches, which is not desirable. The adhoc_searchhead = true (on the current captain) setting will have no effect, as this setting is ignored by the captain. The captain_is_adhoc_searchhead = true (on all members) setting will have no effect, as this setting is only applied to the current captain. For more information, see Configure the captain as an ad hoc search head in the Splunk documentation.

 In a distributed environment, knowledge object bundles are replicated from the search head to the SPLUNK_HOME/var/run/searchpeers directory on the search peer(s). A knowledge object bundle is a compressed file that contains the knowledge objects, such as fields, lookups, macros, and tags, that are required for a search. A search peer is a Splunk instance that provides data to a search head in a distributed search. A search head is a Splunk instance that coordinates and executes a search across multiple search peers. When a search head initiates a search, it creates a knowledge object bundle and replicates it to the search peers that are involved in the search. The search peers store the knowledge object bundle in the SPLUNK_HOME/var/run/searchpeers directory, which is a temporary directory that is cleared when the Splunk service restarts. The search peers use the knowledge object bundle to apply the knowledge objects to the data and return the results to the search head. The SPLUNK_HOME/var/lib/searchpeers, SPLUNK_HOME/var/log/searchpeers, and SPLUNK_HOME/var/spool/searchpeers directories are not the locations where the knowledge object bundles are replicated, because they do not exist in the Splunk file system

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

References:

• About the app key value store
• KV Store and search head clusters

• KV Store is a feature of Splunk Enterprise that allows apps to store and retrieve data within the context of an app1.
• KV Store resides on search heads and replicates data across the members of a search head cluster1.
• KV Store resiliency refers to the ability of KV Store to maintain data availability and consistency in the event of failures or disruptions2.
• One of the factors that affects KV Store resiliency is the network latency between search heads, which can impact the speed and reliability of data replication2.
• Decreasing latency between search heads can improve KV Store resiliency by reducing the chances of data loss, inconsistency, or corruption2.
• The other options are not directly related to KV Store resiliency. Faster storage, indexer CPU and memory, and Operations Log size may affect other aspects of Splunk performance, but not KV Store345.

References: 1: About the app key value store 2: Configure and deploy KV Store using Splunk Enterprise 3: Creating and CRUDing a KV Store in Splunk: Part 1 4: KV store troubleshooting tools 5: Solved: Re: Disabling KV store

According to the Splunk blog1, the Universal Forwarder is ideal for collecting data from high-velocity data sources, such as a syslog server, due to its smaller footprint and faster performance. The Universal Forwarder performs minimal processing and sends raw or unparsed data to the indexers, reducing the network traffic and the load on the forwarders. The other options are false because:

• When most of the data requires masking, a Heavy Forwarder is needed, as it can perform advanced filtering and data transformation before forwarding the data2.
• When data comes directly from a database server, a Heavy Forwarder is needed, as it can run modular inputs such as DB Connect to collect data from various databases2.
• When a modular input is needed, a Heavy Forwarder is needed, as the Universal Forwarder does not include a bundled version of Python, which is required for most modular inputs2.

The best practice for ingesting syslog data from network devices on port 514 into Splunk is to configure syslog to write logs and use a Splunk forwarder to collect the logs. This practice will ensure that the data is reliably collected and forwarded to Splunk, without losing any data or overloading the Splunk indexer. Configuring syslog to send the data to multiple Splunk indexers will not guarantee data reliability, as syslog is a UDP protocol that does not provide acknowledgment or delivery confirmation. Using a Splunk indexer to collect a network input on port 514 directly will not provide data reliability or load balancing, as the indexer may not be able to handle the incoming data volume or distribute it to other indexers. Using a Splunk forwarder to collect the input on port 514 and forward the data will not provide data reliability, as the forwarder may not be able to receive the data from syslog or buffer it in case of network issues. For more information, see [Get data from TCP and UDP ports] and [Best practices for syslog data] in the Splunk documentation.

According to the Splunk documentation1, determining data capacity for an index is a complex task that depends on several factors, such as:

• Average size of event data. This is the average number of bytes per event that you send to Splunk. The larger the events, the more storage space they require and the more indexing time they consume.
• Number of data sources. This is the number of different types of data that you send to Splunk, such as logs, metrics, network packets, etc. The more data sources you have, the more diverse and complex your data is, and the more processing and parsing Splunk needs to do to index it.
• Peak data rates. This is the maximum amount of data that you send to Splunk per second, minute, hour, or day. The higher the peak data rates, the more load and pressure Splunk faces to index the data in a timely manner.

The other option is false because:

• Number of concurrent searches on data. This is not a factor that affects daily indexing volume, as it is related to the search performance and the search scheduler, not the indexing process. However, it can affect the overall resource utilization and the responsiveness of Splunk2.

According to the Splunk documentation1, the Search Job Inspector is a tool that you can use to troubleshoot search performance and understand the behavior of knowledge objects, such as event types, tags, lookups, and so on, within the search. You can inspect search jobs that are currently running or that have finished recently. The Search Job Inspector can help you investigate error messages that appear underneath the search bar in Splunk Web, as it can show you the details of the search job, such as the search string, the search mode, the search timeline, the search log, the search profile, and the search properties. You can use this information to identify the cause of the error and fix it2. The other options are false because:

• Dashboard panels showing “Waiting for queued job to start” on page load is not a problem that can be investigated using the Search Job Inspector, as it indicates that the search job has not started yet. This could be due to the search scheduler being busy or the search priority being low. You can use the Jobs page or the Monitoring Console to monitor the status of the search jobs and adjust the priority or concurrency settings if needed3.
• Different users seeing different extracted fields from the same search is not a problem that can be investigated using the Search Job Inspector, as it is related to the user permissions and the knowledge object sharing settings. You can use the Access Controls page or the Knowledge Manager to manage the user roles and the knowledge object visibility4.
• Events not being sorted in reverse chronological order is not a problem that can be investigated using the Search Job Inspector, as it is related to the search syntax and the sort command. You can use the Search Manual or the Search Reference to learn how to use the sort command and its options to sort the events by any field or criteria.

A dedicated deployment server is a Splunk instance that manages the distribution of configuration updates and apps to a set of deployment clients, such as forwarders, indexers, or search heads. A dedicated deployment server should be used when there are more than 50 deployment clients, because this number exceeds the recommended limit for a non-dedicated deployment server. A non-dedicated deployment server is a Splunk instance that also performs other roles, such as indexing or searching. Using a dedicated deployment server can improve the performance, scalability, and reliability of the deployment process. Option C is the correct answer. Option A is incorrect because the number of search peers does not affect the need for a dedicated deployment server. Search peers are indexers that participate in a distributed search. Option B is incorrect because the number of apps to deploy does not affect the need for a dedicated deployment server. Apps are packages of configurations and assets that provide specific functionality or views in Splunk. Option D is incorrect because the number of server classes does not affect the need for a dedicated deployment server. Server classes are logical groups of deployment clients that share the same configuration updates and apps12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Aboutdeploymentserver  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Whentousedeploymentserver

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

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

According to the Splunk documentation1, one of the benefits of search head clustering is the automatic replication of user knowledge objects, such as dashboards, reports, alerts, and tags. This ensures that all cluster members have the same set of knowledge objects and can serve the same search results to the users. The other options are false because:

• Allowing indexers to maintain multiple searchable copies of all data is a benefit of indexer clustering, not search head clustering2.
• Input settings are not synchronized between search heads, as search head clusters do not collect data from inputs. Data collection is done by forwarders or independent search heads3.
• Fewer network ports are not required to be opened between search heads, as search head clusters use several ports for communication and replication among the members4.

The correct answer is A. Change frozenTimePeriodInSecs to a larger value. This is a possible solution to reduce the need to thaw buckets, as it increases the time period before a bucket is frozen and removed from the index1. The frozenTimePeriodInSecs attribute specifies the maximum age, in seconds, of the data that the index can contain1. By setting it to a larger value, the Splunk administrator can keep the data in the index for a longer time, and avoid having to thaw the buckets frequently. The other options are not effective solutions to reduce the need to thaw buckets. Option B, changing maxTotalDataSizeMB to a smaller value, would actually increase the need to thaw buckets, as it decreases the maximum size, in megabytes, of an index2. This means that the index would reach its size limit faster, and more buckets would be frozen and removed. Option C, changing maxHotSpanSecs to a larger value, would not affect the need to thaw buckets, as it only changes the maximum lifetime, in seconds, of a hot bucket3. This means that the hot bucket would stay hot for a longer time, but it would not prevent the bucket from being frozen eventually. Option D, changing coldToFrozenDir to a different location, would not reduce the need to thaw buckets, as it only changes the destination directory for the frozen buckets4. This means that the buckets would still be frozen and removed from the index, but they would be stored in a different location. Therefore, option A is the correct answer, and options B, C, and D are incorrect.

1: Set a retirement and archiving policy 2: Configure index size 3: Bucket rotation and retention 4: Archive indexed data

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