Pass the Splunk Enterprise Certified Architect SPLK-2002 Questions and answers with Dumpstech

Data retention is optional in the data source assessment because it is not directly related to the ingestion process. Data retention is determined by the index configuration and the storage capacity of the Splunk platform. Data format, data location, and data volume are all essential information for planning how to collect, parse, and index the data source.

The Splunk Capacity Planning Manual defines the total required storage for indexes as a function of daily ingest rate, compression factor, retention period, and an additional padding buffer for index management and growth.

The formula is:

Storage = (Daily Data * Compression Factor * Retention Days) + Padding

Given the values:

Daily rate = 20 GB

Compression factor = 0.5 (50% reduction)

Retention period = 30 days

Padding = 100 GB

Plugging these into the formula gives:

20 * 0.5 * 30 + 100 = 400 GB

This result represents the estimated storage needed to retain 30 days of compressed indexed data with an additional buffer to accommodate growth and Splunk’s bucket management overhead.

Compression factor values typically range between 0.5 and 0.7 for most environments, depending on data type. Using compression in calculations is critical, as indexed data consumes less space than raw input after Splunk’s tokenization and compression processes.

Other options either misapply the compression ratio or the order of operations, producing incorrect totals.

References (Splunk Enterprise Documentation):

• Capacity Planning for Indexes – Storage Sizing and Compression Guidelines

• Managing Index Storage and Retention Policies

• Splunk Enterprise Admin Manual – Understanding Index Bucket Sizes

• Indexing Performance and Storage Optimization Guide

According to the Indexer Clustering Administration Guide, the most efficient and Splunk-recommended way to configure and manage receiving ports for all clustered indexers (peer nodes) is through the Cluster Manager (previously known as the Master Node).

In a clustered environment, configuration changes that affect all peer nodes—such as receiving port definitions—should be managed centrally. The correct procedure is to define the inputs configuration file (inputs.conf) within the Cluster Manager’s manager-apps directory. Specifically, the configuration is placed in:

$SPLUNK_HOME/etc/manager-apps/_cluster/local/inputs.conf

and then deployed to all peers using the configuration bundle push mechanism.

This centralized approach ensures consistency across all peer nodes, prevents manual configuration drift, and allows Splunk to maintain uniform ingestion behavior across the cluster.

Running splunk enable listen on each peer (Option C) or manually configuring inputs via Splunk Web (Option A) introduces inconsistencies and is not recommended in clustered deployments. Using the deployment-apps path (Option B) is meant for deployment servers, not for cluster management.

References (Splunk Enterprise Documentation):

• Indexer Clustering: Configure Peer Nodes via Cluster Manager

• Deploy Configuration Bundles from the Cluster Manager

• inputs.conf Reference – Receiving Data Configuration

• Splunk Enterprise Admin Manual – Managing Clustered Indexers

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.

When converting from a single-site to a multi-site cluster, existing single-site clustered buckets will maintain replication as required according to the single-site policies, but never age out. Single-site clustered buckets are buckets that were created before the conversion to a multi-site cluster. These buckets will continue to follow the single-site replication and search factors, meaning that they will have the same number of copies and searchable copies across the cluster, regardless of the site. These buckets will never age out, meaning that they will never be frozen or deleted, unless they are manually converted to multi-site buckets. Single-site clustered buckets will not continue to replicate within the origin site, because they will be distributed across the cluster according to the single-site policies. Single-site clustered buckets will not be replicated across all peers in the multi-site cluster, because they will follow the single-site replication factor, which may be lower than the multi-site total replication factor. Single-site clustered buckets will not stop replicating within the single-site and remain on the indexer they reside on, because they will still be subject to the replication and availability rules of the cluster

 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.

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.

 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

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

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