SPLK-2002 Splunk Enterprise Certified Architect Questions and Answers

Data Model acceleration is a feature that enables faster searches over large data sets by summarizing the raw data into a more efficient format. Data Model acceleration consumes additional disk space, as it stores both the raw data and the summarized data. The amount of disk space required depends on the size and complexity of the Data Model, the retention period of the summarized data, and the compression ratio of the data. According to the Splunk Enterprise Security Planning and Installation Manual, Data Model acceleration is one of the factors that strongly impacts storage sizing requirements for Enterprise Security. The other factors are the volume and type of data sources, the retention policy of the data, and the replication factor and search factor of the index cluster. The number of scheduled (correlation) searches, the number of Splunk users configured, and the number of source types used in the environment are not directly related to storage sizing requirements for Enterprise Security1

1: https://docs.splunk.com/Documentation/ES/6.6.0/Install/Plan#Storage_sizing_requirements

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 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

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

The correct configuration to meet the customer’s requirements is site_replication_factor = origin:1, site4:1, total:5. This means that each bucket will have one copy at the origin site, one copy at the disaster recovery site (site4), and three copies at any other sites. The total number of copies will be five, as required by the customer. The site_replication_factor determines how many copies of each bucket are stored across the sites in a multisite indexer cluster1. The site_search_factor determines how many copies of each bucket are searchable across the sites in a multisite indexer cluster2. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Configure the site replication factor 2: Configure the site search factor

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

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

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

The splunk rebuild command is used for thawing the archive bucket. Thawing is the process of restoring frozen data back to Splunk for searching. Frozen data is data that has been archived or deleted from Splunk after reaching the end of its retention period. To thaw a bucket, the user needs to copy the bucket from the archive location to the thaweddb directory under SPLUNK_HOME/var/lib/splunk and run the splunk rebuild command to rebuild the .tsidx files for the bucket. The splunk collect command is used for collecting diagnostic data from a Splunk instance. The splunk convert command is used for converting configuration files from one format to another. The splunk dbinspect command is used for inspecting the status and properties of the buckets in an index.

 When 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 deployer distributes apps and non-search related and manual configuration file changes to the search head cluster members. The deployer does not bootstrap a clean Splunk install for a search head cluster, as this is done by the captain. The deployer also does not distribute runtime knowledge object changes made by users across the search head cluster, as this is done by the replication factor. For more information, see Use the deployer to distribute apps and configuration updates in the Splunk documentation.

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

• Greatly reduce WAN traffic by preferentially searching assigned site (search affinity). This means that if you configure each site so that it has both a search head and a full set of searchable data, the search head on each site will limit its searches to local peer nodes. This eliminates any need, under normal conditions, for search heads to access data on other sites, greatly reducing network traffic between sites2.
• Seamlessly route searches to a redundant site in case of a site failure. This means that by storing copies of your data at multiple locations, you maintain access to the data if a disaster strikes at one location. Multisite clusters provide site failover capability. If a site goes down, indexing and searching can continue on the remaining sites, without interruption or loss of data2.

The other options are false because:

• Use blockchain technology to audit search activity from geographically dispersed data centers. This is not a use case of multi-site clustering, as Splunk does not use blockchain technology to audit search activity. Splunk uses its own internal logs and metrics to monitor and audit search activity3.
• Enable a forwarder to send data to multiple indexers. This is not a use case of multi-site clustering, as forwarders can send data to multiple indexers regardless of whether they are in a single-site or multi-site cluster. This is a basic feature of forwarders that allows load balancing and high availability of data ingestion4.

This is a mandatory Splunk component when implementing a search head cluster, as it is responsible for distributing the configuration updates and app bundles to the cluster members1. The deployer is a separate instance that communicates with the cluster manager and pushes the changes to the search heads1. The other options are not mandatory components for a search head cluster. Option A, Captain Server, is not a component, but a role that is dynamically assigned to one of the search heads in the cluster2. The captain coordinates the replication and search activities among the cluster members2. Option C, Cluster Manager, is a component for an indexer cluster, not a search head cluster3. The cluster manager manages the replication and search factors, and provides a web interface for monitoring and managing the indexer cluster3. Option D, RAFT Server, is not a component, but a protocol that is used by the search head cluster to elect the captain and maintain the cluster state4. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Use the deployer to distribute apps and configuration updates 2: About the captain 3: About the cluster manager 4: How a search head cluster works

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

• Multi-site policies will apply only to the data created after migration, unless you configure the manager node to convert legacy buckets to multi-site1.
• All peer nodes do not need to run the same version of Splunk, as long as they are compatible with the manager node2.
• Single-site buckets can be converted to multi-site buckets by changing the constrain_singlesite_buckets setting in the manager node’s server.conf file to "false"1.

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

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

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

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.

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

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

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

 The Splunk instance with the given settings in SPLUNK_HOME/etc/system/local/server.conf is missing the master_uri attribute and needs to be restarted. The master_uri attribute is required for the master node to communicate with the peer nodes and the search head cluster. The master_uri attribute specifies the host name and port number of the master node. Without this attribute, the master node cannot function properly. The Splunk instance also needs to be restarted for the changes in the server.conf file to take effect. The replication_factor setting determines how many copies of each bucket are maintained across the peer nodes. The search factor is a separate setting that determines how many searchable copies of each bucket are maintained across the peer nodes. The search factor is not specified in the given settings, so it defaults to the same value as the replication factor, which is 2. This is not a multi-site cluster, because the site attribute is not specified in the clustering stanza. A multi-site cluster is a cluster that spans multiple geographic locations, or sites, and has different replication and search factors for each site.

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

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

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

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

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

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

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

 Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching. Search head clustering has the following characteristics:

• A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.
• At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.
• The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.

Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.

 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.

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

A best practice to maximize indexing performance is to minimize configuration generality. Configuration generality refers to the use of generic or default settings for data inputs, such as source type, host, index, and timestamp. Minimizing configuration generality means using specific and accurate settings for each data input, which can reduce the processing overhead and improve the indexing throughput. Using automatic source typing, using the Splunk default settings, and not using pre-trained source types are examples of configuration generality, which can negatively affect the indexing performance

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

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

 A multi-site indexer cluster can be configured by directly editing SPLUNK_HOME/etc/system/local/server.conf or running a splunk edit cluster-config command from the CLI. These methods allow the administrator to specify the site attribute for each indexer node and the site_replication_factor and site_search_factor for the cluster. Configuring a multi-site indexer cluster via Splunk Web or directly editing SPLUNK_HOME/etc/system/default/server.conf are not supported methods. For more information, see Configure the indexer cluster with server.conf in the Splunk documentation.

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

To expand the search head cluster by adding a new member, node2, the first step is to initialize the cluster configuration on node2 using the splunk init shcluster-config command. This command sets the required parameters for the cluster member, such as the management URI, the replication port, and the shared secret key. The management URI must be unique for each cluster member and must match the URI that the deployer uses to communicate with the member. The replication port must be the same for all cluster members and must be different from the management port. The secret key must be the same for all cluster members and must be encrypted using the splunk _encrypt command. The master_uri parameter is optional and specifies the URI of the cluster captain. If not specified, the cluster member will use the captain election process to determine the captain. Option C shows the correct syntax and parameters for the splunk init shcluster-config command. Option A is incorrect because the splunk bootstrap shcluster-config command is used to bring up the first cluster member as the initial captain, not to add a new member. Option B is incorrect because the master_uri parameter is not required and the mgmt_uri parameter is missing. Option D is incorrect because the splunk add shcluster-member command is used to add an existing search head to the cluster, not to initialize a new member12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/DistSearch/SHCdeploymentoverview#Initialize_cluster_members  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/DistSearch/SHCconfigurationdetails#Configure_the_cluster_members

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

• inputs.conf is a configuration file that contains settings for various types of data inputs, such as files, directories, network ports, scripts, and so on1.
• initCrcLength is a setting that specifies the number of characters that the input uses to calculate the CRC (cyclic redundancy check) of a file1. The CRC is a value that uniquely identifies a file based on its content2.
• crcSalt is another setting that adds a string to the CRC calculation to force the input to consume files that have matching CRCs1. This can be useful when files have identical headers or when files are renamed or rolled over2.
• When troubleshooting a situation where some files within a directory are not being indexed, the ignored files are discovered to have long headers, the first thing that should be added to inputs.conf is to increase the value of initCrcLength. This is because by default, the input only performs CRC checks against the first 256 bytes of a file, which means that files with long headers may have matching CRCs and be skipped by the input2. By increasing the value of initCrcLength, the input can use more characters from the file to calculate the CRC, which can reduce the chances of CRC collisions and ensure that different files are indexed3.
• Option C is the correct answer because it reflects the best practice for troubleshooting this situation. Option A is incorrect because decreasing the value of initCrcLength would make the CRC calculation less reliable and more prone to collisions. Option B is incorrect because adding a crcSalt with a static string would not help differentiate files with long headers, as they would still have matching CRCs. Option D is incorrect because adding a crcSalt with the attribute would add the full directory path to the CRC calculation, which would not help if the files are in the same directory2.

References:

1: inputs.conf - Splunk Documentation 2: How the Splunk platform handles log file rotation 3: Solved: Configure CRC salt - Splunk Community

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

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

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

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

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

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

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

In the context of splunkd.log events written to the _internal index, the field that identifies the specific log channel is the "channel" field. This information is confirmed by the Splunk Common Information Model (CIM) documentation, where "channel" is listed as a field name associated with Splunk Audit Logs​​.

The following logs are included in the _introspection index, which contains data that the Splunk Enterprise deployment logs for platform instrumentation:

• disk_objects.log. This log contains information about the disk objects that Splunk creates and manages, such as buckets, indexes, and files. This log can help monitor the disk space usage and the bucket lifecycle.
• resource_usage.log. This log contains information about the resource usage of Splunk processes, such as CPU, memory, disk, and network. This log can help monitor the Splunk performance and identify any resource bottlenecks. The following logs are not included in the _introspection index, but rather in the _internal index, which contains data that Splunk generates for internal logging:
• audit.log. This log contains information about the audit events that Splunk records, such as user actions, configuration changes, and search activity. This log can help audit the Splunk operations and security.
• metrics.log. This log contains information about the performance metrics that Splunk collects, such as data throughput, data latency, search concurrency, and search duration. This log can help measure the Splunk performance and efficiency. For more information, see About Splunk Enterprise logging and [About the _introspection index] in the Splunk documentation.

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.

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.

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

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

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.

 The tailing_processor.log file would be the best place to search if you suspect there is a problem interpreting a regular expression in a monitor stanza. This log file contains information about how Splunk monitors files and directories, including any errors or warnings related to parsing the monitor stanza. The splunkd.log file contains general information about the Splunk daemon, but it may not have the specific details about the monitor stanza. The btool.log file contains information about the configuration files, but it does not log the runtime behavior of the monitor stanza. The metrics.log file contains information about the performance metrics of Splunk, but it does not log the event breaking issues. For more information, see About Splunk Enterprise logging 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