Splunk SPLK-2002 Exam Dumps & Practice Test Questions

Question 1:

In a Splunk Enterprise Security indexer cluster consisting of N indexers, which configuration change would yield the largest decrease in overall disk storage requirements?

A. Set the search factor to N-1
B. Increase the number of buckets for each index
C. Reduce the range of data model acceleration
D. Set the replication factor to N-1

Correct Answer: D

Explanation:

When managing storage in a Splunk indexer cluster, reducing disk space consumption often comes down to managing redundancy. In Splunk, one of the most significant contributors to disk usage is the replication factor (RF). This setting determines how many identical copies of indexed data are stored across the cluster’s indexers. For example, an RF of 3 means that three copies of each bucket are stored for fault tolerance.

By adjusting the RF to N-1, where N represents the number of indexers, you reduce the total number of data copies stored. This directly decreases the amount of disk space required. Though this approach minimizes redundancy, it is effective in environments where storage efficiency takes precedence over fault tolerance.

Let’s examine the other choices:

• A. Search factor to N-1: The search factor (SF) controls how many searchable copies of data exist. Reducing this might save some space, but it compromises the system’s ability to quickly respond to searches in the event of indexer failure. It doesn’t significantly lower total disk use like adjusting RF does.

• B. Increase number of buckets: Having more buckets may improve data segmentation and indexing performance, but it doesn’t meaningfully reduce disk usage. It may even increase overhead in some scenarios.

• C. Reduce data model acceleration range: Data model acceleration precomputes and stores summaries to speed up search performance. Lowering the range can reduce the amount of stored accelerated data, but this only affects a subset of total storage and doesn’t provide the scale of savings that adjusting RF offers.

In summary, decreasing the replication factor to N-1 has the most profound impact on overall disk storage because it directly reduces the number of redundant data copies stored across the cluster.

Question 2:

If a system's top priority is maintaining consistent access to searchable data, which strategy should be used to meet this goal?

A. Increase the search factor in the cluster
B. Increase the replication factor in the cluster
C. Add more search heads to the cluster
D. Add more CPU cores to indexer nodes

Correct Answer: B

Explanation:

Ensuring high availability of searchable data in a clustered Splunk environment requires a strategy that offers both redundancy and resilience. The replication factor (RF) plays a crucial role in this, as it controls how many copies of raw indexed data are stored across the indexers in the cluster. By increasing the replication factor, you ensure that there are more redundant copies of the data. This means if one or more nodes go offline due to hardware failure or maintenance, other nodes can still serve the data for search queries.

This level of redundancy is vital for organizations that cannot tolerate data inaccessibility or downtime. The replicated data allows the system to maintain full functionality even when parts of the cluster are unavailable.

Let’s compare the other options:

• A. Increase the search factor: The search factor determines how many searchable copies (i.e., primaries) of data buckets are maintained. While important, increasing the RF typically provides greater fault tolerance since it includes full data replication, not just searchable primaries.

• C. Add more search heads: Adding more search heads might enhance query concurrency and distribute the user load more efficiently, but it does not improve data redundancy. If data on indexers becomes unavailable, additional search heads cannot resolve that issue.

• D. Add more CPUs to indexers: More processing power helps with indexing speed and query execution but does not affect data availability. It improves performance, not fault tolerance.

Therefore, the most reliable way to maintain high availability of searchable data is by increasing the replication factor, ensuring the data is available from multiple indexers even if one or more fail. This directly supports the goal of data availability without impacting the system's searchability.

Question 3:

In a distributed Splunk setup, dashboards in the Monitoring Console show that the environment is reaching its search capacity. 

What action would most effectively improve search performance?

A. Upgrade the indexer drives to SSDs
B. Add more search heads and assign users based on their search patterns
C. Identify and reschedule slow searches for off-peak times
D. Increase the number of search peers and balance data input across them

Correct Answer: D

Explanation:

When a distributed Splunk environment begins to hit its search capacity, the most effective and scalable way to improve performance is by increasing the number of search peers (indexers) and ensuring that data forwarding is balanced evenly across them. This strategy directly enhances the environment’s ability to manage and execute searches by distributing the search workload more effectively.

Indexers (also referred to as search peers) are crucial to the Splunk architecture because they handle both the indexing of incoming data and respond to search requests from search heads. When the number of indexers is limited, search requests can bottleneck due to overburdened resources. By adding more indexers, the system gains more processing power to handle search jobs in parallel, which drastically improves response times.

Even data distribution is equally important. If forwarders send a disproportionate amount of data to specific indexers, it can lead to uneven indexing loads and skewed search performance. Ensuring that data is spread evenly allows for better utilization of resources and avoids creating performance hotspots.

Option A, replacing storage with SSDs, may improve disk I/O but doesn’t address the architectural bottleneck in search distribution. Option B, adding more search heads, might help in very specific cases but doesn’t solve indexing pressure. Option C, rescheduling searches, is a temporary relief strategy, not a capacity solution.

In conclusion, adding search peers and ensuring data is evenly distributed across indexers is a direct, scalable, and sustainable approach for improving search performance in a distributed Splunk deployment nearing its limits.

Question 4:

A Splunk architect finds that logs for a particular web sourcetype are inconsistently formatted. Some data flows through heavy forwarders while other data is managed by another team using different forwarders. 

What is the most probable reason for this formatting inconsistency?

A. Search heads and indexers are configured differently
B. Forwarders are not uniformly configured for data inputs
C. Heavy forwarders and indexers have mismatched settings
D. The forwarders used by the other department are outdated

Correct Answer: B

Explanation:

The inconsistency in log formatting described in this situation is most likely due to differences in data input configuration across multiple forwarders. In a Splunk deployment, forwarders play a key role in ingesting and, in some cases, preprocessing log data before sending it to indexers. If each forwarder is configured differently — for example, with variations in sourcetypes, field extractions, or timestamp parsing rules — it can result in inconsistently formatted events even if the log source is the same.

Heavy forwarders are capable of parsing data before forwarding it, which means they apply transformations like field extractions, line breaking, or timestamp assignments. If a heavy forwarder processes data one way and another forwarder (possibly a universal forwarder or one with different configurations) sends the same type of data unprocessed, the resulting indexed events can appear significantly different. This leads to user confusion and difficulties when performing searches or creating dashboards.

Option A, configuration differences between search heads and indexers, generally doesn’t impact raw data formatting, since parsing decisions are typically made before indexing. Option C, suggesting configuration mismatches between indexers and heavy forwarders, would affect how data is stored or searched, but it’s less likely to cause inconsistencies in event structure. Option D, older forwarder versions, may introduce parsing issues if critical features are missing, but it is not the most probable root cause unless known compatibility issues exist.

Ultimately, inconsistent data input settings, especially when multiple teams manage forwarders independently, are a common source of formatting discrepancies. Ensuring standardized configurations across all forwarders — including consistent sourcetype assignments and parsing logic — is essential for maintaining uniform event structure in Splunk environments.

Question 5:

A customer has installed a 500GB Enterprise license and later added a 300GB No Enforcement license on the same license master. 

Under this setup, what happens to the search functionality once the total data ingestion exceeds the licensed limits?

A. 300GB. After this limit, search is locked out.
B. 500GB. After this limit, search is locked out.
C. 800GB. After this limit, search is locked out.
D. Search is not locked out. Violations are still recorded.

Correct Answer: D

Explanation:

In Splunk licensing, different license types impose varying restrictions, especially when it comes to how much data can be ingested and what actions the system takes when thresholds are exceeded. In this scenario, the customer has installed two license types on the same license master: a 500GB Enterprise license and a 300GB No Enforcement license, making a total of 800GB in licensed volume.

The Enterprise license enforces strict ingestion limits. If data ingestion exceeds the licensed volume over a rolling 30-day period, the system may impose restrictions—most notably on the search functionality. Once those thresholds are breached, some features might be temporarily disabled until the license violation is resolved.

On the other hand, a No Enforcement license behaves differently. As the name suggests, this type does not impose a hard enforcement mechanism. Even if the ingested data surpasses the specified threshold (300GB in this case), Splunk continues to operate normally. The only caveat is that license violations are logged, and the organization may receive alerts or be flagged for potential compliance issues.

When both licenses coexist, Splunk honors the cumulative volume (in this case, 800GB). However, the No Enforcement license overrides any lockout behavior because Splunk does not automatically disable search features based on this license type. Instead, the system merely records the overage as a license violation without restricting the actual use of search or ingestion capabilities.

Thus, even if the ingestion goes beyond 800GB, search remains functional, although the environment will reflect a violation that could be reviewed during audits or compliance checks.

Therefore, the correct answer is D—search functionality is not locked out, and violations are simply recorded for monitoring and compliance.

Question 6:

When managing a Splunk Search Head Cluster (SHC), which tasks are performed by the Deployer? (Select all that apply.)

A. Distributes applications to the members of the Search Head Cluster
B. Performs the initial installation of Splunk on all SHC nodes
C. Deploys manually created or non-search-related configuration files
D. Synchronizes runtime knowledge object changes across the cluster

Correct Answers: A, C

Explanation:

In Splunk's Search Head Cluster (SHC) architecture, the Deployer plays a crucial role in ensuring consistency and reliability across all search head members. The SHC model is designed to provide scalability, load balancing, and high availability for search workloads. The Deployer is not part of the cluster itself but acts as an external node responsible for pushing configurations and applications to the cluster members.

A. Distributes applications to SHC members:
This is correct. One of the primary roles of the Deployer is to distribute apps—both Splunk-native and custom-built—to every search head in the cluster. This ensures that all nodes operate with the same logic, dashboards, and custom functionality, eliminating inconsistencies that might otherwise cause erratic behavior or mismatched search results.

B. Performs the initial installation of Splunk:
This is incorrect. The Deployer does not install Splunk software. Each node in the SHC must already have Splunk installed and configured to be part of the cluster. The Deployer’s function only begins once the cluster is in place and ready to receive configurations or apps.

C. Deploys manually created or non-search-related configuration files:
Correct. The Deployer also handles the distribution of configuration files that are not directly tied to search operations. These could include server configurations, authentication settings, or other admin-defined preferences. As long as the configurations are not dynamic, the Deployer ensures they’re consistently applied across the SHC.

D. Synchronizes runtime knowledge object changes:
Incorrect. Runtime knowledge objects—such as saved searches, dashboards, and alerts created by users—are replicated across the cluster by SHC’s own internal replication mechanisms, not the Deployer. These changes happen dynamically and do not require deployment from an external node.

In conclusion, the Deployer is essential for distributing applications and non-runtime configurations, ensuring operational uniformity across the SHC. The correct answers are A and C.

Question 7:

When configuring Splunk to properly recognize multi-line events in a props.conf file using the LINE_BREAKER setting, which value should be assigned to the SHOULD_LINEMERGE attribute to ensure lines are combined into a single event?

A. Auto
B. None
C. True
D. False

Answer: C

Explanation:

In Splunk, event parsing and line merging are critical aspects of data ingestion, especially when dealing with logs that span multiple lines. The props.conf file enables administrators to define how incoming data should be interpreted. One common challenge is ensuring that multi-line events are treated as a single event rather than being split incorrectly across multiple entries. This is where the SHOULD_LINEMERGE and LINE_BREAKER attributes come into play.

The LINE_BREAKER setting uses a regular expression to identify where new events begin. While this helps separate events based on custom delimiters, Splunk also needs to know whether to merge lines that do not match the start pattern. This is controlled using the SHOULD_LINEMERGE attribute.

Setting SHOULD_LINEMERGE = true instructs Splunk to attempt to merge multiple lines into a single event using the LINE_BREAKER rule as guidance. This is especially important for logs where events naturally extend over multiple lines, such as Java exception traces, detailed logs, or stack dumps. Without enabling line merging, each new line might be misinterpreted as a separate event, leading to fragmented, inaccurate indexing and analysis.

By contrast, setting SHOULD_LINEMERGE = false will tell Splunk to treat every new line as a separate event unless another structured event processor is defined (e.g., using LINE_BREAKER without merging). The options “Auto” and “None” are not valid values for this attribute in Splunk's configuration syntax and would result in errors or unintended behavior.

Thus, when the goal is to correctly capture a single logical event that spans multiple lines, setting SHOULD_LINEMERGE to true is necessary. This ensures data coherence and accuracy during ingestion, making it easier to query, visualize, and analyze logs within Splunk.

Question 8:

When developing a deployment plan for an IT system or infrastructure project, which of the following components are essential to include? (Select all that apply)

A. Business continuity and disaster recovery plans
B. Documentation of current logging practices and data source inventory
C. Diagrams outlining current and future topology
D. A complete list of all relevant stakeholders

Answer: A, B, C, D

Explanation:

A comprehensive deployment plan plays a critical role in the success of implementing new systems or upgrading existing infrastructure. It outlines every aspect needed to transition smoothly from planning to operational use. To achieve this, several foundational components must be documented and considered.

A. Business continuity and disaster recovery plans are essential. They provide strategies to ensure service availability during unexpected events and lay out how the system can recover from catastrophic failures. Including these plans in your deployment documentation ensures you're not only focusing on implementation but also resilience.

B. Logging practices and an inventory of data sources must also be part of the deployment plan. Logging helps in tracking system performance and diagnosing issues, while maintaining an accurate list of data sources ensures completeness during integration. This also assists in compliance and audit readiness.

C. Topology diagrams—both current and future—are valuable for visualizing the infrastructure. These diagrams show how different system components interact and evolve, helping to plan for scaling, identify single points of failure, and communicate the system layout to all stakeholders clearly.

D. Stakeholder lists are often underestimated but are crucial. Knowing who the key players are—users, managers, technical leads, vendors—helps align expectations, communicate changes effectively, and resolve issues quickly. Identifying indirect stakeholders, like security or legal teams, ensures nothing is overlooked during deployment.

By incorporating all four elements, your deployment plan addresses technical, operational, and business considerations. This holistic approach reduces risk, ensures smoother transitions, and improves collaboration across teams. Missing any of these components could lead to miscommunications, downtime, or incomplete deployments, which can be costly both in time and resources.

Question 9:

Which methods can be utilized to configure a multi-site indexer cluster in Splunk? (Choose all that apply.)

A. Using the Splunk Web interface
B. Manually editing the server.conf file located at SPLUNK_HOME/etc/system/local/
C. Executing the splunk edit cluster-config command via the command-line interface (CLI)
D. Editing the server.conf file located at SPLUNK_HOME/etc/system/default/ directly

Answer: A, B, C

Explanation:

Configuring a multi-site indexer cluster in Splunk involves setting up multiple indexers across different sites (such as geographically dispersed data centers) to ensure data availability, redundancy, and fault tolerance. This process requires careful adjustment of cluster-related settings.

One common way to configure a multi-site cluster is through the Splunk Web interface (A). This GUI offers administrators a straightforward way to manage cluster settings like site definitions, replication factors, and search factors without delving into command-line intricacies. This method is user-friendly and ideal for those who prefer a graphical approach.

Another method is manual editing of the server.conf file located at SPLUNK_HOME/etc/system/local/ (B). This configuration file holds custom settings that override default parameters. Editing this file allows fine-grained control over cluster attributes such as site names and behaviors specific to multi-site deployments.

A third option is using the CLI command splunk edit cluster-config (C). This command enables administrators to modify cluster configurations programmatically, which is particularly useful in automated or scripted deployments. It provides direct control over cluster parameters such as replication and search factors.

On the other hand, directly editing server.conf in the default directory (D) is discouraged. Files in SPLUNK_HOME/etc/system/default/ contain default settings that can be overwritten during upgrades, causing configuration loss. Custom changes should always be placed in the local directory to ensure persistence.

In summary, methods A, B, and C are valid and commonly used approaches for configuring a multi-site indexer cluster, while D should be avoided.

Question 10:

Which index-time props.conf attributes in Splunk have a direct effect on indexing performance? (Select all that apply.)

A. REPORT
B. LINE_BREAKER
C. ANNOTATE_PUNCT
D. SHOULD_LINEMERGE

Answer: B, D

Explanation:

In Splunk, the props.conf file controls how incoming data is parsed and indexed. Several attributes within this file impact the efficiency of indexing by influencing event segmentation and processing.

The LINE_BREAKER (B) attribute determines how Splunk identifies the end of one event and the beginning of another, especially when processing multiline events like logs or stack traces. This attribute uses regular expressions to define line breaks. If the regex pattern is complex or inefficient, it can significantly slow down indexing as Splunk spends more resources evaluating where events break.

Another important attribute is SHOULD_LINEMERGE (D), which dictates whether Splunk attempts to combine multiple lines into a single event. Multiline events improve data integrity by preserving context, but merging lines requires additional computation. This overhead can affect indexing speed, particularly for large volumes of data or when event boundaries are difficult to detect.

In contrast, REPORT (A) refers to referencing field extractions or event transformations during indexing. While such operations can affect overall system performance, REPORT itself does not directly control the speed of indexing. Instead, it mainly influences search-time efficiency and event enrichment.

Similarly, ANNOTATE_PUNCT (C) allows Splunk to mark punctuation in events for better parsing or presentation during searches. However, this attribute does not have a significant impact on the indexing throughput or resource consumption during data ingestion.

In conclusion, LINE_BREAKER and SHOULD_LINEMERGE directly impact indexing performance by affecting event segmentation logic. Optimizing these settings is crucial for maintaining fast and efficient data ingestion, while REPORT and ANNOTATE_PUNCT primarily influence post-indexing processing.