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

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.

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 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.

The following may explain the problem of why a colleague cannot see the src_ip field in their search results: The field was extracted as a private knowledge object, and the colleague did not explicitly use the field in the search and the search was set to Fast Mode. A knowledge object is a Splunk entity that applies some knowledge or intelligence to the data, such as a field extraction, a lookup, or a macro. A knowledge object can have different permissions, such as private, app, or global. A private knowledge object is only visible to the user who created it, and it cannot be shared with other users. A field extraction is a type of knowledge object that extracts fields from the raw data at index time or search time. If a field extraction is created as a private knowledge object, then only the user who created it can see the extracted field in their search results. A search mode is a setting that determines how Splunk processes and displays the search results, such as Fast, Smart, or Verbose. Fast mode is the fastest and most efficient search mode, but it also limits the number of fields and events that are displayed. Fast mode only shows the default fields, such as _time, host, source, sourcetype, and _raw, and any fields that are explicitly used in the search. If a field is not used in the search and it is not a default field, then it will not be shown in Fast mode. The events are tagged as communicate, but are missing the network tag, and the Typing Queue, which does regular expression replacements, is blocked, are not valid explanations for the problem. Tags are labels that can be applied to fields or field values to make them easier to search. Tags do not affect the visibility of fields, unless they are used as filters in the search. The Typing Queue is a component of the Splunk data pipeline that performs regular expression replacements on the data, such as replacing IP addresses with host names. The Typing Queue does not affect the field extraction process, unless it is configured to do so

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 splunk clean kvstore command is used to clear the KV store. This command will delete all the collections and documents in the KV store and reset it to an empty state. This command can be useful for troubleshooting KV store issues or resetting the KV store data. The splunk clear kvstore, splunk delete kvstore, and splunk reinitialize kvstore commands are not valid Splunk commands. For more information, see Use the CLI to manage the KV store in the Splunk documentation.

 When planning a search head cluster, the following statement is true: All indexers must belong to the underlying indexer cluster (no standalone indexers). A search head cluster is a group of search heads that share configurations, apps, and search jobs. A search head cluster requires an indexer cluster as its data source, meaning that all indexers that provide data to the search head cluster must be members of the same indexer cluster. Standalone indexers, or indexers that are not part of an indexer cluster, cannot be used as data sources for a search head cluster. All search heads do not have to use the same operating system, as long as they are compatible with the Splunk version and the indexer cluster. All search heads do not have to be members of the cluster, as standalone search heads can also search the indexer cluster, but they will not have the benefits of configuration replication and load balancing. The search head captain does not have to be assigned to the largest search head in the cluster, as the captain is dynamically elected from among the cluster members based on various criteria, such as CPU load, network latency, and search load.

All of the options are possible causes of a crash in Splunk. According to the Splunk documentation1, incorrect ulimit settings can lead to file descriptor exhaustion, which can cause Splunk to crash or hang. Insufficient disk IOPS can also cause Splunk to crash or become unresponsive, as Splunk relies heavily on disk performance2. Insufficient memory can cause Splunk to run out of memory and crash, especially when running complex searches or handling large volumes of data3. Running out of disk space can cause Splunk to stop indexing data and crash, as Splunk needs enough disk space to store its data and logs4.

1: Configure ulimit settings for Splunk Enterprise 2: Troubleshoot Splunk performance issues 3: Troubleshoot memory usage 4: Troubleshoot disk space issues

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: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Enabledebuglogging  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Serverlogging  3: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Loglocalcfg

 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.

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

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

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 TailingProcessor channel in the splunkd.log file would help troubleshoot this issue, because it contains information about the files that Splunk monitors and indexes, such as the file path, size, modification time, and CRC checksum. It also logs any errors or warnings that occur during the file monitoring process, such as permission issues, file rotation, or file truncation. The TailingProcessor channel can help identify if Splunk is reading the new data from the monitor input file or not, and what might be causing the problem. Option B is the correct answer. Option A is incorrect because the ModularInputs channel logs information about the modular inputs that Splunk uses to collect data from external sources, such as scripts, APIs, or custom applications. It does not log information about the monitor input file. Option C is incorrect because the ChunkedLBProcessor channel logs information about the load balancing process that Splunk uses to distribute data among multiple indexers. It does not log information about the monitor input file. Option D is incorrect because the ArchiveProcessor channel logs information about the archive process that Splunk uses to move data from the hot/warm buckets to the cold/frozen buckets. It does not log information about the monitor input file12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/WhatSplunklogsaboutitself#splunkd.log  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket#Check_the_splunkd.log_file

 The following artifacts are included in a Splunk diag file:

• Server specs. These are the specifications of the server that Splunk runs on, such as the CPU model, the memory size, the disk space, and the network interface. These specs can help understand the Splunk hardware requirements and performance.
• Current open connections. These are the connections that Splunk has established with other Splunk instances or external sources, such as forwarders, indexers, search heads, license masters, deployment servers, and data inputs. These connections can help understand the Splunk network topology and communication.
• Internal Splunk log files. 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.
• Index listings. These are the listings of the indexes that Splunk has created and configured, such as the index name, the index location, the index size, and the index attributes. These listings can help understand the Splunk data management and retention. The following artifacts are not included in a Splunk diag file:
• Search history. This is the history of the searches that Splunk has executed, such as the search query, the search time, the search results, and the search user. This history is not part of the Splunk diag file, but it can be accessed from the Splunk Web interface or the audit.log file.
• Splunk users and their roles. These are the users that Splunk has created and assigned roles to, such as the user name, the user password, the user role, and the user capabilities. These users and roles are not part of the Splunk diag file, but they can be accessed from the Splunk Web interface or the authentication.conf and authorize.conf files.
• KV store listings. These are the listings of the KV store collections and documents that Splunk has created and stored, such as the collection name, the collection schema, the document ID, and the document fields. These listings are not part of the Splunk diag file, but they can be accessed from the Splunk Web interface or the mongod.log file.
• Indexed 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 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 activate replication for an index in an indexer cluster, the repFactor attribute must be configured in indexes.conf on all peer nodes. This attribute specifies the replication factor for the index, which determines how many copies of raw data are maintained by the cluster. Setting the repFactor attribute to auto will enable replication for the index. The replicate attribute in indexes.conf is not a valid Splunk attribute. The repFactor attribute in outputs.conf and the replicate attribute in deploymentclient.conf are not related to replication for an index in an indexer cluster. For more information, see Configure indexes for indexer clusters in the Splunk documentation.

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

• main is the default index for user data, not internal data3.
• _telemetry is an index that contains data collected by the Splunk Telemetry feature, which sends anonymous usage and performance data to Splunk4.
• _introspection is an index that contains data collected by the Splunk Monitoring Console, which monitors the health and performance of Splunk components.

According to the Splunk documentation1, the CHARSET setting in props.conf specifies the character set encoding of the source data. This setting has the least impact on indexing performance, as it only affects how Splunk interprets the bytes of the data, not how it processes or transforms the data. The other options are false because:

• The SHOULD_LINEMERGE setting in props.conf determines whether Splunk breaks events based on timestamps or newlines. This setting has a significant impact on indexing performance, as it affects how Splunk parses the data and identifies the boundaries of the events2.
• The TRUNCATE setting in props.conf specifies the maximum number of characters that Splunk indexes from a single line of a file. This setting has a moderate impact on indexing performance, as it affects how much data Splunk reads and writes to the index3.
• The TIME_PREFIX setting in props.conf specifies the prefix that directly precedes the timestamp in the event data. This setting has a moderate impact on indexing performance, as it affects how Splunk extracts the timestamp and assigns it to the event

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 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.

 The index-time props.conf attributes that impact indexing performance are LINE_BREAKER and SHOULD_LINEMERGE. These attributes determine how Splunk breaks the incoming data into events and whether it merges multiple events into one. These operations can affect the indexing speed and the disk space consumption. The REPORT attribute does not impact indexing performance, as it is used to apply transforms at search time. The ANNOTATE_PUNCT attribute does not impact indexing performance, as it is used to add punctuation metadata to events at search time. For more information, see [About props.conf and transforms.conf] in the Splunk documentation.

 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

The correct answer is B. Set the Replication Factor based on allowed indexer failure. This is a best practice for adding data resiliency to a single-site indexer cluster, as it ensures that there are enough copies of each bucket to survive the loss of one or more indexers without affecting the searchability of the data1. The Replication Factor is the number of copies of each bucket that the cluster maintains across the set of peer nodes2. The Replication Factor should be set according to the number of indexers that can fail without compromising the cluster’s ability to serve data1. For example, if the cluster can tolerate the loss of two indexers, the Replication Factor should be set to three1.

The other options are not best practices for adding data resiliency. Option A, setting the Replication Factor to 49, is not recommended, as it would create too many copies of each bucket and consume excessive disk space and network bandwidth1. Option C, always using the default Replication Factor of 3, is not optimal, as it may not match the customer’s requirements and expectations for data availability and performance1. Option D, setting the Replication Factor based on allowed search head failure, is not relevant, as the Replication Factor does not affect the search head availability, but the searchability of the data on the indexers1. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Configure the replication factor 2: About indexer clusters and index replication

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

 When adding or rejoining a member to a search head cluster, and the following error is displayed: Error pulling configurations from the search head cluster captain; consider performing a destructive configuration resync on this search head cluster member.

The corrective action that should be taken is to run the splunk resync shcluster-replicated-config command on this member. This command will delete the existing configuration files on this member and replace them with the latest configuration files from the captain. This will ensure that the member has the same configuration as the rest of the cluster. Restarting the search head, running the splunk apply shcluster-bundle command from the deployer, or running the clean raft command on all members of the search head cluster are not the correct actions to take in this scenario. For more information, see Resolve configuration inconsistencies across cluster members in the Splunk documentation.

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.

Decreasing the data model acceleration range will reduce the disk size requirements for a cluster of indexers running Splunk Enterprise Security. Data model acceleration creates tsidx files that consume disk space on the indexers. Reducing the acceleration range will limit the amount of data that is accelerated and thus save disk space. Setting the cluster search factor or replication factor to N-1 will not reduce the disk size requirements, but rather increase the risk of data loss. Increasing the number of buckets per index will also increase the disk size requirements, as each bucket has a minimum size. For more information, see Data model acceleration and Bucket size 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.

A monitored log file is changing on the forwarder, but Splunk searches are not finding any new data that has been added. This could be caused by two possible reasons: B. An admin has removed the Splunk fishbucket on the forwarder. C. The last 256 bytes of the monitored file are not changing. Option B is correct because the Splunk fishbucket is a directory that stores information about the files that have been monitored by Splunk, such as the file name, size, modification time, and CRC checksum. If an admin removes the fishbucket, Splunk will lose track of the files that have been previously indexed and will not index any new data from those files. Option C is correct because Splunk uses the CRC checksum of the last 256 bytes of a monitored file to determine if the file has changed since the last time it was read. If the last 256 bytes of the file are not changing, Splunk will assume that the file is unchanged and will not index any new data from it. Option A is incorrect because running the splunk clean eventdata -index  command on the indexer will delete all the data from the specified index, but it will not affect the forwarder’s ability to send new data to the indexer. Option D is incorrect because Splunk does not use the first 256 bytes of a monitored file to determine if the file has changed12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Data/Monitorfilesanddirectories  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket

To ensure that high-velocity sources will not have forwarding delays to the indexers, the default limit for maxKBps in limits.conf should be increased. This parameter controls the maximum bandwidth that a forwarder can use to send data to the indexers. By default, it is set to 256 KBps, which may not be sufficient for high-volume data sources. Increasing this limit can reduce the forwarding latency and improve the performance of the forwarders. However, this should be done with caution, as it may affect the network bandwidth and the indexer load. Option B is the correct answer. Option A is incorrect because the sessionTimeout parameter in server.conf controls the duration of a TCP connection between a forwarder and an indexer, not the bandwidth limit. Option C is incorrect because the forceTimebasedAutoLB parameter in outputs.conf controls the frequency of load balancing among the indexers, not the bandwidth limit. Option D is incorrect because the phoneHomelntervallnSecs parameter in deploymentclient.conf controls the interval at which a forwarder contacts the deployment server, not the bandwidth limit12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Limitsconf#limits.conf.spec  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Forwarding/Routeandfilterdatad#Set_the_maximum_bandwidth_usage_for_a_forwarder

Searching for closed_txn=0 in this search filters results to situations where Splunk was started, but not stopped. This means that the transaction was not completed, and Splunk crashed before it could finish the pipelines. The closed_txn field is added by the transaction command, and it indicates whether the transaction was closed by an event that matches the endswith condition1. A value of 0 means that the transaction was not closed, and a value of 1 means that the transaction was closed1. Therefore, option D is the correct answer, and options A, B, and C are incorrect.

1: transaction command overview

The client filters available in serverclass.conf are DNS name, IP address, and platform (machine type). These filters allow the administrator to specify which forwarders belong to a server class and receive the apps and configurations from the deployment server. The Splunk server role is not a valid client filter in serverclass.conf, as it is not a property of the forwarder. For more information, see [Use forwarder management filters] in the Splunk documentation.

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 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.

This is the fall-back method for Splunk if .delta replication fails during knowledge bundle replication. Knowledge bundle replication is the process of distributing the knowledge objects, such as lookups, macros, and field extractions, from the search head cluster to the indexer cluster1. Splunk uses two methods of knowledge bundle replication: .delta replication and .bundle replication1. .Delta replication is the default and preferred method, as it only replicates the changes or updates to the knowledge objects, which reduces the network traffic and disk space usage1. However, if .delta replication fails for some reason, such as corrupted files or network errors, Splunk automatically switches to .bundle replication, which replicates the entire knowledge bundle, regardless of the changes or updates1. This ensures that the knowledge objects are always synchronized between the search head cluster and the indexer cluster, but it also consumes more network bandwidth and disk space1. The other options are not valid fall-back methods for Splunk. Option A, restarting splunkd, is not a method of knowledge bundle replication, but a way to restart the Splunk daemon on a node2. This may or may not fix the .delta replication failure, but it does not guarantee the synchronization of the knowledge objects. Option B, .delta replication, is not a fall-back method, but the primary method of knowledge bundle replication, which is assumed to have failed in the question1. Option D, restarting mongod, is not a method of knowledge bundle replication, but a way to restart the MongoDB daemon on a node3. This is not related to the knowledge bundle replication, but to the KV store replication, which is a different process3. Therefore, option C is the correct answer, and options A, B, and D are incorrect.

1: How knowledge bundle replication works 2: Start and stop Splunk Enterprise 3: Restart the KV store

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

This is where in the Job Inspector details can be found to help determine where performance is affected, as it shows the time and resources spent by each component of the search, such as commands, subsearches, lookups, and post-processing1. The Execution Costs > Components section can help identify the most expensive or inefficient parts of the search, and suggest ways to optimize or improve the search performance1. The other options are not as useful as the Execution Costs > Components section for finding performance issues. Option A, Search Job Properties > runDuration, shows the total time, in seconds, that the search took to run2. This can indicate the overall performance of the search, but it does not provide any details on the specific components or factors that affected the performance. Option B, Search Job Properties > runtime, shows the time, in seconds, that the search took to run on the search head2. This can indicate the performance of the search head, but it does not account for the time spent on the indexers or the network. Option C, Job Details Dashboard > Total Events Matched, shows the number of events that matched the search criteria3. This can indicate the size and scope of the search, but it does not provide any information on the performance or efficiency of the search. Therefore, option D is the correct answer, and options A, B, and C are incorrect.

1: Execution Costs > Components 2: Search Job Properties 3: Job Details Dashboard

 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

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 index=_internal component=DC* host= search will show all deployment client messages from the universal forwarder. The component field indicates the type of Splunk component that generated the message, and the host field indicates the host name of the machine that sent the message. The index=_audit component=DC* host= search will not return any results, because the deployment client messages are not stored in the _audit index. The index=_internal component=DS* host= search will show the deployment server messages from the deployment server, not the client. The index=_audit component=DS* host= search will also not return any results, for the same reason as above

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 __introspection log file contains data about the impact of the Splunk software on the host system, such as CPU, memory, disk, and network usage, as well as KV store performance1. This log file is monitored by default and the contents are sent to the _introspection index1. The other options are not related to the __introspection log file. File monitor input configurations are stored in inputs.conf2. File monitor checkpoint offset is stored in fishbucket3. User activities and knowledge objects are stored in the _audit and _internal indexes respectively4.