SPLK-2002 Exam Dumps - Splunk Enterprise Certified Architect

 The following statements are true about Splunk indexer clustering:

All peer nodes must run exactly the same Splunk version. This is a requirement for indexer clustering, as different Splunk versions may have different data formats or features that are incompatible with each other. All peer nodes must run the same Splunk version as the master node and the search heads that connect to the cluster.

The search head must run the same or a later Splunk version than the peer nodes. This is a recommendation for indexer clustering, as a newer Splunk version may have new features or bug fixes that improve the search functionality or performance. The search head should not run an older Splunk version than the peer nodes, as this may cause search errors or failures. The following statements are false about Splunk indexer clustering:

The master node must run the same or a later Splunk version than the search heads. This is not a requirement or a recommendation for indexer clustering, as the master node does not participate in the search process. The master node should run the same Splunk version as the peer nodes, as this ensures the cluster compatibility and functionality.

The peer nodes must run the same or a later Splunk version than the master node. This is not a requirement or a recommendation for indexer clustering, as the peer nodes do not coordinate the cluster activities. The peer nodes should run the same Splunk version as the master node, as this ensures the cluster compatibility and functionality. For more information, see [About indexer clusters and index replication] and [Upgrade an indexer cluster] in the Splunk documentation.

The repFactor (replication factor) attribute in the indexes.conf file determines whether an index participates in indexer clustering and how many copies of its data are replicated across peer nodes.

When repFactor is set to 0, it explicitly instructs Splunk to exclude that index from participating in the cluster replication and management process. This means:

The index is not replicated across peer nodes.

It will not be managed by the Cluster Manager.

It exists only locally on the indexer where it was created.

Such indexes are typically used for local-only storage, such as _internal, _audit, or other custom indexes that store diagnostic or node-specific data.

By contrast:

repFactor=auto allows the index to inherit the cluster-wide replication policy from the Cluster Manager.

available_sites and site_mappings relate to multisite configurations, controlling where copies of the data are stored, but they do not remove the index from clustering.

Setting repFactor=0 is the only officially supported way to create a non-clustered index within a clustered environment.

References (Splunk Enterprise Documentation):

• indexes.conf Reference – repFactor Attribute Explanation

• Managing Non-Clustered Indexes in Clustered Deployments

• Indexer Clustering: Index Participation and Replication Policies

• Splunk Enterprise Admin Manual – Local-Only and Clustered Index Configurations

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

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.

Intermediate forwarders are forwarders that receive data from other forwarders and then send that data to indexers. They can be useful in some scenarios, such as when network bandwidth or security constraints prevent direct forwarding to indexers, or when data needs to be routed, cloned, or modified in transit. However, intermediate forwarders also introduce additional complexity and overhead to the data pipeline, which can affect the performance and reliability of data ingestion. Therefore, intermediate forwarders should be avoided when possible, and used only when there is a clear benefit or requirement for them. Some of the drawbacks of intermediate forwarders are:

They increase the number of hops and connections in the data flow, which can introduce latency and increase the risk of data loss or corruption.

They consume more resources on the hosts where they run, such as CPU, memory, disk, and network bandwidth, which can affect the performance of other applications or processes on those hosts.

They require additional configuration and maintenance, such as setting up inputs, outputs, load balancing, security, monitoring, and troubleshooting.

They can create data duplication or inconsistency if they are not configured properly, such as when using cloning or routing rules.

Some of the references that support this answer are:

Configure an intermediate forwarder, which states: “Intermediate forwarding is where a forwarder receives data from one or more forwarders and then sends that data on to another indexer. This kind of setup is useful when, for example, you have many hosts in different geographical regions and you want to send data from those forwarders to a central host in that region before forwarding the data to an indexer. All forwarder types can act as an intermediate forwarder. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”

Intermediate data routing using universal and heavy forwarders, which states: “This document outlines a variety of Splunk options for routing data that address both technical and business requirements. Overall benefits Using splunkd intermediate data routing offers the following overall benefits: … The routing strategies described in this document enable flexibility for reliably processing data at scale. Intermediate routing enables better security in event-level data as well as in transit. The following is a list of use cases and enablers for splunkd intermediate data routing: … Limitations splunkd intermediate data routing has the following limitations: … Increased complexity and resource consumption. splunkd intermediate data routing adds complexity to the data pipeline and consumes resources on the hosts where it runs. This can affect the performance and reliability of data ingestion and other applications or processes on those hosts. Therefore, intermediate routing should be avoided when possible, and used only when there is a clear benefit or requirement for it.”

Use forwarders to get data into Splunk Enterprise, which states: “The forwarders take the Apache data and send it to your Splunk Enterprise deployment for indexing, which consolidates, stores, and makes the data available for searching. Because of their reduced resource footprint, forwarders have a minimal performance impact on the Apache servers. … Note: You can also configure a forwarder to send data to another forwarder, which then sends the data to the indexer. This is called intermediate forwarding. However, this adds complexity to your deployment and can affect performance, so use it only when necessary.”