The Splunk Enterprise Certified Architect (SPLK-2002)

A lookup table is a file that contains a list of values that can be used to enrich or modify the data during search time1. Lookup tables can be stored in CSV files or in the KV Store1. Troubleshooting lookup tables involves identifying and resolving issues that prevent the lookup tables from being accessed, updated, or applied correctly by the Splunk searches. Some of the tools and methods that can help with troubleshooting lookup tables are:

web_access.log: This is a file that contains information about the HTTP requests and responses that occur between the Splunk web server and the clients2. This file can help troubleshoot issues related to lookup table permissions, availability, and errors, such as 404 Not Found, 403 Forbidden, or 500 Internal Server Error34.

btool output: This is a command-line tool that displays the effective configuration settings for a given Splunk component, such as inputs, outputs, indexes, props, and so on5. This tool can help troubleshoot issues related to lookup table definitions, locations, and precedence, as well as identify the source of a configuration setting6.

search.log: This is a file that contains detailed information about the execution of a search, such as the search pipeline, the search commands, the search results, the search errors, and the search performance. This file can help troubleshoot issues related to lookup table commands, arguments, fields, and outputs, such as lookup, inputlookup, outputlookup, lookup_editor, and so on .

Option B is the correct answer because web_access.log is the best place to start troubleshooting lookup table issues, as it can provide the most relevant and immediate information about the lookup table access and status. Option A is incorrect because btool output is not a log file, but a command-line tool. Option C is incorrect because health.log is a file that contains information about the health of the Splunk components, such as the indexer cluster, the search head cluster, the license master, and the deployment server. This file can help troubleshoot issues related to Splunk deployment health, but not necessarily related to lookup tables. Option D is incorrect because configuration_change.log is a file that contains information about the changes made to the Splunk configuration files, such as the user, the time, the file, and the action. This file can help troubleshoot issues related to Splunk configuration changes, but not necessarily related to lookup tables.

Splunk Enterprise uses the _audit index to log and store all user activity and audit-related information. This includes details such as user logins, searches executed, configuration changes, role modifications, and app management actions.

The _audit index is populated by data collected from the Splunkd audit logger and records actions performed through both Splunk Web and the CLI. Each event in this index typically includes fields like user, action, info, search_id, and timestamp, allowing administrators to track activity across all Splunk users and components for security, compliance, and accountability purposes.

The _internal index, by contrast, contains operational logs such as metrics.log and scheduler.log used for system performance and health monitoring. _kvstore stores internal KV Store metadata, and _telemetry is used for optional usage data reporting to Splunk.

The _audit index is thus the authoritative source for user behavior monitoring within Splunk environments and is a key component of compliance and security auditing.

References (Splunk Enterprise Documentation):

• Audit Logs and the _audit Index – Monitoring User Activity

• Splunk Enterprise Security and Compliance: Tracking User Actions

• Splunk Admin Manual – Overview of Internal Indexes (_internal, _audit, _introspection)

• Splunk Audit Logging and User Access Monitoring

Splunk’s Search Head Clustering documentation explains that the cluster uses a majority-based election system. A captain is elected only when a node sees more than half of the cluster. In a two-site design where site1 has the majority of members, Splunk states that the majority site continues normal operation during a network partition . The minority site (site2) is not allowed to elect a captain and should not promote itself.

Splunk specifically warns administrators not to enable static captain on a minority site during a network split. Doing so creates two independent clusters, leading to configuration divergence and severe data-consistency issues. The documentation emphasizes that static captain should only be used for a complete loss of majority, not for a site partition.

Because Site1 maintains majority, it remains the active cluster and site2 does not perform any actions. Splunk states that minority-site members should simply wait until network communication is restored.

Thus the correct answer is B: No action is required .

 The splunk add cluster-master command is used to configure the same search head to join another indexer cluster. A search head can search multiple indexer clusters by adding multiple cluster-master entries in its server.conf file. The splunk add cluster-master command can be used to add a new cluster-master entry to the server.conf file, by specifying the host name and port number of the master node of the other indexer cluster. The splunk add cluster-config command is used to configure the search head to join the first indexer cluster, not the second one. The splunk edit cluster-config command is used to edit the existing cluster configuration of the search head, not to add a new one. The splunk edit cluster-master command does not exist, and it is not a valid command.

As per Splunk Enterprise Search Performance documentation, the most significant factor affecting search performance when querying across a large number of buckets is disk I/O throughput. A search that spans “All Time” forces Splunk to inspect all historical buckets (hot, warm, cold, and potentially frozen if thawed), even if only a few events match the query. This dramatically increases the amount of data read from disk, making the search bound by I/O performance rather than CPU or memory.

Increasing the number of indexing pipelines (Option B) only benefits data ingestion, not search performance. Changing to a real-time search (Option D) does not help because real-time searches are optimized for streaming new data, not historical queries. The indexed_realtime_use_by_default setting (Option C) applies only to streaming indexed real-time searches, not historical “All Time” searches.

To improve performance for such searches, Splunk documentation recommends enhancing disk I/O capability — typically through SSD storage, increased disk bandwidth, or optimized storage tiers. Additionally, creating summary indexes or accelerated data models may help for repeated “All Time” queries, but the most direct improvement comes from faster disk performance since Splunk must scan large numbers of buckets for even small result sets.

References (Splunk Enterprise Documentation):

• Search Performance Tuning and Optimization

• Understanding Bucket Search Mechanics and Disk I/O Impact

• limits.conf Parameters for Search Performance

• Storage and Hardware Sizing Guidelines for Indexers and Search Heads

When adding or decommissioning a member from a Search Head Cluster (SHC), the proper order of operations is:

Delete Splunk Enterprise, if it exists.

Install and initialize the instance.

Join the SHC.

This order of operations ensures that the member has a clean and consistent Splunk installation before joining the SHC. Deleting Splunk Enterprise removes any existing configurations and data from the instance. Installing and initializing the instance sets up the Splunk software and the required roles and settings for the SHC. Joining the SHC adds the instance to the cluster and synchronizes the configurations and apps with the other members. The other order of operations are not correct, because they either skip a step or perform the steps in the wrong order.

Splunk Enterprise allows administrators to dynamically increase logging verbosity for a specific search by adding a | noop log_debug=* command immediately after the base search. This method provides temporary, search-specific debug logging without requiring global configuration changes or restarts.

The noop (no operation) command passes all results through unchanged but can trigger internal logging actions. When paired with the log_debug=* argument, it instructs Splunk to record detailed debug-level log messages for that specific search execution in search.log and the relevant internal logs.

This approach is officially documented for troubleshooting complex search issues such as:

Unexpected search behavior or slow performance.

Field extraction or command evaluation errors.

Debugging custom search commands or macros.

Using this method is safer and more efficient than modifying server-wide logging configurations (server.conf or limits.conf), which can affect all users and increase log noise. The “Server logging” page in Splunk Web (Option D) adjusts global logging levels, not per-search debugging.

References (Splunk Enterprise Documentation):

• Search Debugging Techniques and the noop Command

• Understanding search.log and Per-Search Logging Control

• Splunk Search Job Inspector and Debugging Workflow

• Troubleshooting SPL Performance and Field Extraction Issues

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.

Splunk Enterprise documentation clearly states that the best method to secure log traffic between Universal Forwarders (UFs) and Indexers is to implement Transport Layer Security (TLS) using signed SSL certificates. When Universal Forwarders send data to Indexers, this communication can be encrypted using SSL/TLS to prevent eavesdropping, data tampering, or interception while in transit.

Splunk provides default self-signed certificates out of the box, but these are only for testing or lab environments and should not be used in production. Production-grade security requires custom, signed SSL certificates — either from an internal Certificate Authority (CA) or a trusted public CA. These certificates validate both the sender (forwarder) and receiver (indexer), ensuring data integrity and authenticity.

In practice, this involves:

Generating or obtaining CA-signed certificates.

Configuring the forwarder’s outputs.conf to use SSL encryption (sslCertPath, sslPassword, and sslRootCAPath).

Configuring the indexer’s inputs.conf and server.conf to require and validate client certificates.

This configuration ensures end-to-end encryption for all log data transmitted from forwarders to indexers.

Routing traffic through a WAF (Option C) does not provide end-to-end encryption for Splunk’s internal communication, and securing search head–to–indexer communication (Option D) is unrelated to forwarder data flow.

References (Splunk Enterprise Documentation):

• Securing Splunk Enterprise: Encrypting Data in Transit Using SSL/TLS

• Configure Forwarder-to-Indexer Encryption

• Server and Forwarder Authentication with Signed Certificates

• Best Practices for Forwarder Management and Security Configuration