If .delta replication fails during knowledge bundle replication, what is the fall-back method for Splunk?
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 works2: Start and stop Splunk Enterprise3: Restart the KV store
Which of the following strongly impacts storage sizing requirements for Enterprise Security?
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
New data has been added to a monitor input file. However, searches only show older data.
Which splunkd. log channel would help troubleshoot this issue?
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.log2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Troubleshooting/Didyouloseyourfishbucket#Check_the_splunkd.log_file
Which Splunk internal index contains license-related events?
The _internal index contains license-related events, such as the license usage, the license quota, the license pool, the license stack, and the license violations. These events are logged by the license manager in the license_usage.log file, which is part of the _internal index. The _audit index contains audit events, such as user actions, configuration changes, and search activity. These events are logged by the audit trail in the audit.log file, which is part of the _audit index. The _license index does not exist in Splunk, as the license-related events are stored in the _internal index. The _introspection index contains platform instrumentation data, such as the resource usage, the disk objects, the search activity, and the data ingestion. These data are logged by the introspection generator in various log files, such as resource_usage.log, disk_objects.log, search_activity.log, and data_ingestion.log, which are part of the _introspection index. For more information, seeAbout Splunk Enterprise loggingand [About the _internal index] in the Splunk documentation.
Which Splunk component is mandatory when implementing a search head cluster?
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 updates2: About the captain3: About the cluster manager4: How a search head cluster works
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