SPLK-2002 Exam Dumps

Explanation:
The master node distributes configuration bundles to peer nodes in the slaveapps 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.

Explanation:
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.

Explanation: When Splunk indexes data in a non-clustered environment, it creates index and .tsidx files by default. The index files contain the raw data that Splunk has ingested, compressed and encrypted. The .tsidx files contain the time-series index that maps the timestamps and event IDs of the raw data. The rawdata and index files are not the correct terms for the files that Splunk creates. The compressed and .tsidx files are partially correct, but compressed is not the proper name for the index files. The compressed and meta data files are also partially correct, but meta data is not the proper name for the .tsidx files.

Explanation: 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 Security.

Explanation:
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.

Explanation:
A search head cluster is a group of Splunk Enterprise search heads that share configurations, job scheduling, and search artifacts1. The deployer is a Splunk Enterprise instance that distributes apps and other configurations to the cluster members1. The local folder of each Splunk app contains the custom configurations that override the default settings2. The default folder of each Splunk app contains the default configurations that are provided by the app2.
By default, when the deployer pushes an app to the search head cluster, it merges the local folder of the app into the default folder and deploys the merged folder to the search heads3. This means that the custom configurations in the local folder will take precedence over the default settings in the default folder. However, this also means that the local folder of the app on the search heads will be empty, unless the app is modified through the search head UI3.
Option B is the correct answer because it reflects the default behavior of the deployer when pushing apps to the search head cluster. Option A is incorrect because the local folder is not copied to the local folder on the search heads, but merged into the default folder.
Option C is incorrect because all the .conf files in the local folder are deployed to the search heads, not only certain ones. Option D is incorrect because the local folder is not ignored, but merged into the default folder.

Explanation:
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.