Splunk SPLK-2002 Splunk Enterprise Certified Architect Exam Practice Test

According to the Splunk documentation1, any search head in the cluster can update the KV store collection. The KV store collection is replicated across all the cluster members, and any write operation is delegated to the KV store captain, who then synchronizes the changes with the other members. The KV store primary search head is not a valid term, as there is no such role in a search head cluster. The other options are false because:

• The search head cluster captain is not the only node that can update the KV store collection, as any member can initiate a write operation1.
• Any search head except the captain can also update the KV store collection, as the write operation will be delegated to the captain1.

 The Monitoring Console is the Splunk tool that offers a health check for administrators to evaluate the health of their Splunk deployment. The Monitoring Console provides dashboards and alerts that show the status and performance of various Splunk components, such as indexers, search heads, forwarders, license usage, and search activity. The Monitoring Console can also run health checks on the deployment and identify any issues or recommendations. The btool is a command-line tool that shows the effective settings of the configuration files, but it does not offer a health check. The DiagGen is a tool that generates diagnostic snapshots of the Splunk environment, but it does not offer a health check. The SPL Clinic is a tool that analyzes and optimizes SPL queries, but it does not offer a health check. For more information, see About the Monitoring Console in the Splunk documentation.

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

The KV store forms its own cluster within a SHC. The maximum number of SHC members KV store will form is 50. The KV store cluster is a subset of the SHC members that are responsible for replicating and storing the KV store data. The KV store cluster can have up to 50 members, but only 20 of them can be active at any given time. The other members are standby members that can take over if an active member fails. The KV store cluster cannot have more than 50 members, nor can it have an unlimited number of members. The KV store cluster cannot have 25 or 100 members, because these numbers are not multiples of 5, which is the minimum replication factor for the KV store 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.

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

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Updating/Configuredeploymentclients  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/Wheretofindtheconfigurationfiles

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

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

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

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.

 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.

According to the Splunk documentation1, a bucket within a clustered index contains two key types of files: the raw data in compressed form (rawdata) and the indexes that point to the raw data (tsidx files). A bucket can be either replicated or searchable, depending on whether it has both types of files or only the rawdata file. A replicated bucket is a bucket that has been copied from one peer node to another for the purpose of data replication. A searchable bucket is a bucket that has both the rawdata and the tsidx files, and can be searched by the search heads. The types of files that exist in a bucket within a clustered index are:

• Inside a searchable bucket, there is tsidx and rawdata. This is true because a searchable bucket contains both the data and the index files, and can be searched by the search heads1.
• Inside a replicated bucket, there is both tsidx and rawdata. This is true because a replicated bucket can also be a searchable bucket, if it has both the data and the index files. However, not all replicated buckets are searchable, as some of them might only have the rawdata file, depending on the replication factor and the search factor settings1.

The other options are false because:

• Inside a replicated bucket, there is only rawdata. This is false because a replicated bucket can also have the tsidx file, if it is a searchable bucket. A replicated bucket only has the rawdata file if it is a non-searchable bucket, which means that it cannot be searched by the search heads until it gets the tsidx file from another peer node1.
• Inside a searchable bucket, there is only tsidx. This is false because a searchable bucket always has both the tsidx and the rawdata files, as they are both required for searching the data. A searchable bucket cannot exist without the rawdata file, as it contains the actual data that the tsidx file points to1.

Primary rebalancing automatically occurs when a rolling restart completes, a master node rejoins the cluster, or a peer node joins or rejoins the cluster. These events can cause the distribution of primary buckets to become unbalanced, so the master node will initiate a rebalancing process to ensure that each peer node has roughly the same number of primary buckets. Primary rebalancing does not occur when a captain joins or rejoins the cluster, because the captain is a search head cluster component, not an indexer cluster component. The captain is responsible for search head clustering, not indexer clustering

The minimum reference server specification for a Splunk indexer is 12 CPU cores, 12GB RAM, and 800 IOPS. This specification is based on the assumption that the indexer will handle an average indexing volume of 100GB per day, with a peak of 300GB per day, and a typical search load of 1 concurrent search per 1GB of indexing volume. The other specifications are either higher or lower than the minimum requirement. For more information, see [Reference hardware] 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

• Number of concurrent users: This is an important factor because it affects the search performance and resource utilization of the Splunk environment. More users mean more concurrent searches, which require more CPU, memory, and disk I/O. The number of concurrent users also determines the search head capacity and the search head clustering configuration12
• Volume of incoming data: This is another crucial factor because it affects the indexing performance and storage requirements of the Splunk environment. More data means more indexing throughput, which requires more CPU, memory, and disk I/O. The volume of incoming data also determines the indexer capacity and the indexer clustering configuration13
• Existence of premium apps: This is a relevant factor because some premium apps, such as Splunk Enterprise Security and Splunk IT Service Intelligence, have additional requirements and recommendations for the Splunk environment. For example, Splunk Enterprise Security requires a dedicated search head cluster and a minimum of 12 CPU cores per search head. Splunk IT Service Intelligence requires a minimum of 16 CPU cores and 64 GB of RAM per search head45

References:

1: Splunk Validated Architectures 2: Search head capacity planning 3: Indexer capacity planning 4: Splunk Enterprise Security Hardware and Software Requirements 5: [Splunk IT Service Intelligence Hardware and Software Requirements]

The Monitoring Console (MC) is the Splunk Enterprise monitoring tool that lets you view detailed topology and performance information about your Splunk Enterprise deployment1. The MC can be installed on any Splunk Enterprise instance that can access the data from all the instances in the deployment2. However, following the Splunk recommendations, the MC should be installed on the search head cluster deployer, which is a dedicated instance that manages the configuration bundle for the search head cluster members3. This way, the MC can monitor the search head cluster as well as the indexer cluster and the forwarders, without affecting the performance or availability of the other instances4. The other options are not recommended because they either introduce additional load on the existing instances (such as A and D) or do not have access to the data from the search head cluster (such as B).

1: About the Monitoring Console - Splunk Documentation 2: Add Splunk Enterprise instances to the Monitoring Console 3: Configure the deployer - Splunk Documentation 4: [Monitoring Console setup and use - 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

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

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

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.

Indexed extraction configurations are processed in the indexing phase of the Splunk Enterprise data pipeline. The data pipeline is the process that Splunk uses to ingest, parse, index, and search data. Indexed extraction configurations are settings that determine how Splunk extracts fields from data at index time, rather than at search time. Indexed extraction can improve search performance, but it also increases the size of the index. Indexed extraction configurations are applied in the indexing phase, which is the phase where Splunk writes the data and the .tsidx files to the index. The input phase is the phase where Splunk receives data from various sources and formats. The parsing phase is the phase where Splunk breaks the data into events, timestamps, and hosts. The search phase is the phase where Splunk executes search commands and returns results.

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.

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

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 indexing is read/write intensive, as it involves reading data from various sources, writing data to disk, and reading data from disk for searching and reporting. Therefore, it is important to select the appropriate disk storage solution for each deployment, based on the performance, reliability, and cost requirements. The recommended RAID setup for Splunk indexers is RAID 10 (1 + 0), as it provides the best balance of performance and reliability. RAID 10 combines the advantages of RAID 1 (mirroring) and RAID 0 (striping), which means that it offers both data redundancy and data distribution. RAID 10 can tolerate multiple disk failures, as long as they are not in the same mirrored pair, and it can improve the read and write speed, as it can access multiple disks in parallel2

High performance SAN (Storage Area Network) can be used for Splunk indexers, but it is not recommended, as it is more expensive and complex than local disks. SAN also introduces additional network latency and dependency, which can affect the performance and availability of Splunk indexers. SAN is more suitable for Splunk search heads, as they are less read/write intensive and more CPU intensive2

NFS (Network File System) should not be used for storing hot and warm buckets, as it can cause data corruption, data loss, and performance degradation. NFS is a network-based file system that allows multiple clients to access the same files on a remote server. NFS is not compatible with Splunk index replication and search head clustering, as it can cause conflicts and inconsistencies among the Splunk instances. NFS is also slower and less reliable than local disks, as it depends on the network bandwidth and availability. NFS can be used for storing cold and frozen buckets, as they are less frequently accessed and less critical for Splunk operations2

Virtualized environments are not usually preferred over bare metal for Splunk indexers, as they can introduce additional overhead and complexity. Virtualized environments can affect the performance and reliability of Splunk indexers, as they share the physical resources and the network with other virtual machines. Virtualized environments can also complicate the monitoring and troubleshooting of Splunk indexers, as they add another layer of abstraction and configuration. Virtualized environments can be used for Splunk indexers, but they require careful planning and tuning to ensure optimal performance and availability2

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

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

Splunk configuration files are files that contain settings that control various aspects of Splunk behavior, such as data inputs, outputs, indexing, searching, clustering, and so on1. Troubleshooting Splunk configuration files involves identifying and resolving issues that affect the functionality or performance of Splunk due to incorrect or conflicting configuration settings. Some of the tools and methods that can help with troubleshooting Splunk configuration files are:

• 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 performance2. This file can help troubleshoot issues related to search configuration, such as props.conf, transforms.conf, macros.conf, and so on3.
• 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 on4. This tool can help troubleshoot issues related to configuration precedence, inheritance, and merging, as well as identify the source of a configuration setting5.
• diagnostic logs: These are files that contain information about the Splunk system, such as the Splunk version, the operating system, the hardware, the license, the indexes, the apps, the users, the roles, the permissions, the configuration files, the log files, and the metrics6. These files can help troubleshoot issues related to Splunk installation, deployment, performance, and health7.

Option A is the correct answer because crash logs are the least helpful in troubleshooting Splunk configuration files. Crash logs are files that contain information about the Splunk process when it crashes, such as the stack trace, the memory dump, and the environment variables8. These files can help troubleshoot issues related to Splunk stability, reliability, and security, but not necessarily related to Splunk configuration9.

References:

1: About configuration files - Splunk Documentation 2: Use the search.log file - Splunk Documentation 3: Troubleshoot search-time field extraction - Splunk Documentation 4: Use btool to troubleshoot configurations - Splunk Documentation 5: Troubleshoot configuration issues - Splunk Documentation 6: About the diagnostic utility - Splunk Documentation 7: Use the diagnostic utility - Splunk Documentation 8: About crash logs - Splunk Documentation 9: [Troubleshoot Splunk Enterprise crashes - 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

Two indexers clustered is the recommended deployment for a customer who plans to ingest 600 GB of data per day into Splunk, has six concurrent users, and wants high data availability and high search performance. This deployment will provide enough indexing capacity and search concurrency for the customer’s needs, while also ensuring data replication and searchability across the cluster. The customer can also save on the hardware cost by using only two indexers. Two indexers not in a cluster will not provide high data availability, as there is no data replication or failover. Three indexers not in a cluster will provide more indexing capacity and search concurrency, but also more hardware cost and no data availability. The customer’s data retention period, number of long searches, or volume of saved/scheduled searches are not relevant for determining the number of indexers. For more information, see [Reference hardware] and [About indexer clusters and index replication] in the Splunk documentation.

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.

 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

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

• The users having insufficient permissions would result in a different error message, such as “You do not have permission to view this page” or "You do not have permission to run this search"1.
• An add-on needing to be updated would not affect the validity of the sid, unless the add-on changes the search syntax or the data source in a way that makes the search invalid or inaccessible1.
• One or more indexers being down would not cause the “Unknown sid” error, as the sid is stored on the search head, not the indexers. However, it could cause other errors, such as “Unable to distribute to peer” or "Search peer has the following message: not enough disk space"1.

The splunk edit licenser-localslave command is used to convert a Splunk instance to a license slave. This command will configure the Splunk instance to contact a license master and receive a license from it. This command should be used when the Splunk instance is part of a distributed deployment and needs to share a license pool with other instances. The splunk add licenses command is used to add a license to a Splunk instance, not to convert it to a license slave. The splunk list licenser-slaves command is used to list the license slaves that are connected to a license master, not to convert a Splunk instance to a license slave. The splunk list licenser-localslave command is used to list the license master that a license slave is connected to, not to convert a Splunk instance to a license slave. For more information, see Configure license slaves in the Splunk documentation.

• To resync a stale KV Store member in a search head cluster, you need to stop the search head that has the stale KV Store member, run the command splunk clean kvstore --local, and then restart the search head. This triggers the initial synchronization from other KV Store members12.
• The command splunk resync kvstore [-source sourceId] is used to resync the entire KV Store cluster from one of the members, not a single member. This command can only be invoked from the node that is operating as search head cluster captain2.
• The command splunk clean eventdata -local is used to delete all indexed data from a standalone indexer or a cluster peer node, not to resync the KV Store3.
• References:

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

When using ingest-based licensing, there are no Splunk roles that require the license manager to scale, because the license manager does not need to handle any additional load or complexity. Ingest-based licensing is a new licensing model that allows customers to pay for the data they ingest into Splunk, regardless of the data source, volume, or use case. Ingest-based licensing simplifies the licensing process and eliminates the need for license pools, license stacks, license slaves, and license warnings. The license manager is still responsible for enforcing the license quota and generating license usage reports, but it does not need to communicate with any other Splunk instances or monitor their license usage. Therefore, option C is the correct answer. Option A is incorrect because search peers are indexers that participate in a distributed search. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option B is incorrect because search heads are Splunk instances that coordinate searches across multiple indexers. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option D is incorrect because deployment clients are Splunk instances that receive configuration updates and apps from a deployment server. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/AboutSplunklicensing  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/HowSplunklicensingworks

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 deployer is a Splunk Enterprise instance that distributes apps and other configurations to the members of a search head cluster1.
• The deployer cannot share functionality with any other Splunk Enterprise instance, including the license master, the master node, or the monitoring console2.
• However, the search head cluster members can share functionality with the master node and the monitoring console, as long as they are not designated as the captain of the cluster3.
• Therefore, the correct answer is B. License master, as it is the only instance that cannot share functionality with the deployer under any circumstances.

References: 1: About the deployer 2: Deployer system requirements 3: Search head cluster architecture

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.

According to the Splunk documentation1, the Splunk Deployer deploys app content to the etc/slave-apps/ directory on the search head cluster members by default. This directory contains the apps that the deployer distributes to the members as part of the configuration bundle. The other options are false because:

• The etc/apps/ directory contains the apps that are installed locally on each member, not the apps that are distributed by the deployer2.
• The etc/shcluster/ directory contains the configuration files for the search head cluster, not the apps that are distributed by the deployer3.
• The etc/deploy-apps/ directory is not a valid Splunk directory, as it does not exist in the Splunk file system structure4.

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

• Expected daily ingest volumes: This is the amount of data that will be ingested and indexed by the Splunk platform per day. This affects the storage capacity, the indexing performance, and the license usage of the Splunk deployment. The number and size of indexes should be planned according to the expected daily ingest volumes, as well as the peak ingest volumes, to ensure that the Splunk deployment can handle the data load and meet the business requirements12.
• Data retention time policies: This is the duration for which the data will be stored and searchable by the Splunk platform. This affects the storage capacity, the data availability, and the data compliance of the Splunk deployment. The number and size of indexes should be planned according to the data retention time policies, as well as the data lifecycle, to ensure that the Splunk deployment can retain the data for the desired period and meet the legal or regulatory obligations13.
• Access controls: This is the mechanism for granting or restricting access to the data by the Splunk users or roles. This affects the data security, the data privacy, and the data governance of the Splunk deployment. The number and size of indexes should be planned according to the access controls, as well as the data sensitivity, to ensure that the Splunk deployment can protect the data from unauthorized or inappropriate access and meet the ethical or organizational standards14.

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

References:

1: Splunk Validated Architectures 2: [Indexer capacity planning] 3: [Set a retirement and archiving policy for your indexes] 4: [About securing Splunk Enterprise] 5: [Search head capacity planning] : [App installation and management overview]

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

 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.

The master node distributes configuration bundles to peer nodes in the slave-apps directory under $SPLUNK_HOME/etc. The configuration bundle method is the only supported method for managing common configurations and app deployment across the set of peers. It ensures that all peers use the same versions of these files1. Bundles typically contain a subset of files (configuration files and assets) from $SPLUNK_HOME/etc/system, $SPLUNK_HOME/etc/apps, and $SPLUNK_HOME/etc/users2. The process of distributing knowledge bundles means that peers by default receive nearly the entire contents of the search head’s apps3.

According to the Splunk blog1, the best method for sizing or scaling a search head cluster is to estimate the maximum concurrent number of searches and divide by the number of CPU cores per search head. This gives you an idea of how many search heads you need to handle the peak search load without overloading the CPU resources. The other options are false because:

• Estimating the maximum daily ingest volume in gigabytes and dividing by the number of CPU cores per search head is not a good method for sizing or scaling a search head cluster, as it does not account for the complexity and frequency of the searches. The ingest volume is more relevant for sizing or scaling the indexers, not the search heads2.
• Estimating the total number of searches per day and dividing by the number of CPU cores available on the search heads is not a good method for sizing or scaling a search head cluster, as it does not account for the concurrency and duration of the searches. The total number of searches per day is an average metric that does not reflect the peak search load or the search performance2.
• Dividing the number of indexers by three to achieve the correct number of search heads is not a good method for sizing or scaling a search head cluster, as it does not account for the search load or the search head capacity. The number of indexers is not directly proportional to the number of search heads, as different types of data and searches may require different amounts of resources2.

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