• Home
• Splunk Exams
• SPLK-2002 - Splunk Enterprise Certified Architect

SPLK-2002 Splunk Practice Test Questions and Exam Dumps

Question No 1:

Which of the following configurations would result in the most significant reduction in disk space requirements for a cluster of N indexers running Splunk Enterprise Security?

A. Setting the cluster search factor to N-1.
B. Increasing the number of buckets per index.
C. Decreasing the data model acceleration range.
D. Setting the cluster replication factor to N-1.

Answer:The correct answer is 

D. Setting the cluster replication factor to N-1.

Explanation:

In Splunk Enterprise Security, efficient disk space utilization is critical for managing large amounts of data. The disk space consumption in a Splunk indexer cluster is primarily determined by factors like the replication factor (RF), search factor (SF), and the way data is stored (e.g., through index buckets). Let’s explore each option in detail:

A. Setting the cluster search factor to N-1:

The search factor (SF) controls the number of searchable copies of data stored across indexers. The default SF is typically set to 2, meaning two copies of each piece of data are stored in the cluster for redundancy and high availability. Reducing the search factor to N-1 would imply that there are fewer searchable copies, which could improve storage efficiency. However, it also compromises data availability. While it does reduce storage needs, it’s not a recommended configuration because it impacts search availability and fault tolerance. Thus, this setting won’t result in the most significant reduction in disk space.

B. Increasing the number of buckets per index:

Buckets in Splunk represent the storage structure where event data is organized. While increasing the number of buckets may allow for more efficient indexing, it doesn’t significantly impact the overall disk usage in the cluster. The number of buckets influences how data is stored and accessed, but it is not a primary factor in reducing disk space compared to replication or search factors.

C. Decreasing the data model acceleration range:

Data model acceleration is used in Splunk to speed up searches by precomputing data for later use. Decreasing the data model acceleration range can reduce the amount of data that needs to be precomputed and stored, thus reducing disk usage. However, this change mainly affects the storage of accelerated data models, not the overall storage of indexed data across the cluster. The impact on disk space is relatively small compared to adjusting the replication factor.

D. Setting the cluster replication factor to N-1:

The replication factor (RF) dictates how many copies of data are stored across the indexers in the cluster for fault tolerance. In a Splunk cluster, the default replication factor is typically 3, meaning three copies of each data set are stored on different indexers. By reducing the replication factor to N-1, you are decreasing the number of redundant copies of data. This leads directly to a reduction in the amount of disk space needed to store indexed data. While this setting reduces redundancy and fault tolerance, it also leads to a significant reduction in the storage footprint. Reducing the RF is one of the most effective ways to minimize disk space usage in an indexer cluster.

Conclusion:

Among the given options, reducing the replication factor (D) is the most effective strategy for reducing disk space requirements in a Splunk cluster. This approach minimizes the number of data copies stored across the cluster, directly impacting disk space usage without compromising too much on the availability and reliability of the indexed data.

Question No 2:

Stakeholders have emphasized that the primary concern for the system is ensuring high availability of searchable data. 

Which of the following strategies would best meet this requirement?

A. Increasing the search factor in the cluster.
B. Increasing the replication factor in the cluster.
C. Increasing the number of search heads in the cluster.
D. Increasing the number of CPUs on the indexers in the cluster.

Detailed Question with Answer and Explanation:

In systems where high availability and efficient searchability of data are crucial, it is essential to focus on strategies that maintain data accessibility and ensure fast response times, even during periods of high load or system failures. The question asks for the best approach to guarantee that searchable data remains available and accessible at all times in a clustered environment.

Answer: B. Increasing the replication factor in the cluster.

Explanation:

When dealing with large volumes of data, especially in distributed systems or clustered environments, ensuring high availability and searchability of that data is vital. The replication factor refers to the number of copies of each piece of data maintained across different nodes or servers within the cluster. By increasing the replication factor, you ensure that data is available on multiple nodes, which provides redundancy and fault tolerance.

In the event of a node failure, the system can still access the replicated data from other nodes, thus ensuring high availability and minimizing downtime. This is particularly important in scenarios where data needs to be quickly searchable, as the failure of a node will not disrupt the ability to search through the data.

Here’s a breakdown of why other options are less effective in directly addressing the high availability of searchable data:

• A. Increasing the search factor in the cluster: The search factor typically refers to the number of replicas maintained for search-related operations. However, this is not a direct solution to increasing high availability of the data itself. It mainly focuses on search performance rather than data redundancy.
  
  

• C. Increasing the number of search heads in the cluster: Increasing the number of search heads can improve query performance, but it does not directly address the availability of the data itself. If the data is not adequately replicated, adding more search heads won't provide resilience to node failures.
  
  

• D. Increasing the number of CPUs on the indexers in the cluster: More CPUs on indexers can improve indexing performance, but it does not impact the availability of the data. While it may help handle more queries or faster indexing, it doesn’t provide fault tolerance for ensuring that data remains available if nodes fail.

Thus, increasing the replication factor in the cluster is the most effective approach for ensuring that the data remains available and searchable, even in the event of hardware failures or other disruptions.

Question No 3:

In a distributed deployment, the Monitoring Console dashboards indicate that the system is nearing its search capacity. To enhance the search performance, 

Which of the following strategies will likely provide the most significant improvement?

A. Replace the indexer storage with solid-state drives (SSD).
B. Add more search heads and redistribute users based on the search type.
C. Look for slow searches and reschedule them to run during off-peak hours.
D. Add more search peers and ensure forwarders are distributing data evenly across all indexers.

Answer: The most effective strategy for improving search performance in this scenario would be 

D. Add more search peers and ensure forwarders distribute data evenly across all indexers.

Explanation:

When the search performance of a distributed deployment is near capacity, there are several approaches you can consider, each with its benefits and trade-offs.

1. A. Replace the indexer storage with solid-state drives (SSD):
   SSDs can provide faster read and write speeds compared to traditional hard drives (HDDs), which could improve performance in some cases, especially when disk throughput is a bottleneck. However, simply upgrading the storage does not directly address the underlying cause of performance issues, such as the number of indexers or search heads. While SSDs improve storage speed, they are not a comprehensive solution to search performance problems.
   
   

2. B. Add more search heads and redistribute users based on the search type:
   Adding search heads can distribute the search workload among more resources, improving user query experience. However, if the bottleneck lies within the indexing or data distribution rather than the number of search heads, this solution may not provide significant improvement. The performance would improve only if the search heads are the limiting factor, which is not always the case in a distributed environment.
   
   

3. C. Look for slow searches and reschedule them to run during off-peak hours:
   This approach may temporarily alleviate some load during peak times but does not fundamentally solve the capacity issue. Offloading slow searches to off-peak hours can help distribute the load, but if the system is generally nearing capacity, this is only a temporary fix and may not provide a lasting solution for overall search performance.
   
   

4. D. Add more search peers and ensure forwarders distribute data evenly across all indexers:
   This is the most effective long-term solution. Adding more search peers (indexers) increases the parallel processing capability of the system. It allows Splunk to handle larger datasets and more simultaneous search queries efficiently. Ensuring that data is evenly distributed across indexers means that no single indexer becomes a bottleneck, and the system can scale more effectively. This strategy addresses the root cause of the performance issue: the distribution of both search queries and data.

In summary, D offers the most comprehensive solution by addressing both the data distribution and search capacity, ensuring that the system can handle increased load efficiently.

Question No 4:

A Splunk architect has inherited the Splunk deployment at Buttercup Games. End users are reporting that the events for a specific web sourcetype are inconsistently formatted. Upon further investigation, the architect discovers that the web logs are not flowing through the same infrastructure. Some of the web log data passes through heavy forwarders, while others are routed through forwarders managed by a different department. This disparity in data routing seems to be contributing to the inconsistencies in event formatting.

What could be the potential cause of this issue?

A. The search head may have different configurations than the indexers. 

B. The data inputs are not properly configured across all the forwarders. 

C. The indexers may have different configurations than the heavy forwarders. 

D. The forwarders managed by the other department are an older version than the rest.

Answer:

B. The data inputs are not properly configured across all the forwarders.

Explanation:

In this scenario, the issue of inconsistently formatted web logs likely stems from improper or inconsistent configuration of data inputs across the different Splunk forwarders. Splunk forwarders are responsible for collecting, formatting, and forwarding log data to the indexers. If the data inputs are not uniformly configured, it can lead to inconsistent event formatting, especially when different forwarders are used.

Here’s a breakdown of why Option B is the most likely cause:

• Data Inputs Configuration: If different forwarders are configured differently or if there is a misconfiguration in the data input settings (such as sourcetypes, field extractions, or line-breaking configurations), it could result in discrepancies in how the logs are parsed and formatted. This is particularly true if the web logs from various sources (heavy forwarders vs. other departmental forwarders) are processed differently.
  
  

• Heavy Forwarders: Heavy forwarders, which are responsible for parsing and transforming the data before forwarding it to the indexers, might have specific configurations for log parsing that are different from other forwarders. If these configurations are inconsistent, this would lead to formatting issues.
  
  

• Other Options:
  
  

• Option A (Search head and indexer configurations mismatch) is unlikely to be the issue because the search head and indexers mainly handle searching and indexing, not data parsing.
  
  

• Option C (Mismatch between indexers and heavy forwarders) could contribute to issues but is less likely to cause event formatting problems directly. Indexers primarily store and search data.
  
  

• Option D (Older version of forwarders managed by another department) might cause issues, but version mismatches usually affect functionality rather than formatting inconsistencies, unless the version differences impact specific parsing capabilities.

Thus, the root cause is most likely the improper configuration of data inputs on the various forwarders, leading to inconsistent parsing and formatting of the web logs. Ensuring uniformity in the data input configurations across all forwarders will likely resolve the formatting issues.

Question No 5:

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

Given this setup, how much data can the customer ingest before the search functionality is locked out?

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.

Detailed Question with Answer and Explanation:

Answer: D. Search is not locked out. Violations are still recorded.

Explanation:

In the scenario provided, the customer has two types of licenses installed: a 500GB Enterprise license and a 300GB No Enforcement license, both on the same license master. Understanding the impact of these licenses on data ingestion and search behavior is critical.

1. Enterprise License (500GB):
   This type of license generally comes with enforced limits. In the case of a 500GB Enterprise license, the system will allow the customer to ingest up to 500GB of data, beyond which restrictions might apply, such as limiting certain functionalities or blocking access to search.
   
   

2. No Enforcement License (300GB):
   A "no enforcement" license typically refers to a scenario where the system does not actively restrict actions once the licensed data limit is reached. In this case, with the 300GB No Enforcement license, the customer can ingest up to 300GB of data without facing a hard restriction on the ingestion process. However, it’s important to note that while the system will allow ingestion, this license doesn't actively enforce limits on system functionality, such as search.

How Data Ingestion and Search Behavior Interact:
In such a scenario, while the system can handle the 500GB from the Enterprise license and 300GB from the No Enforcement license, it’s essential to know that search functionality will not be immediately locked out when the total data ingestion exceeds the licensed limits.

Search is not blocked outright because the No Enforcement license does not impose hard limitations. However, violations of licensing terms (such as ingesting more than the allowed total of 800GB) will still be logged, and the system may flag these violations for auditing or reporting purposes. This means that while the customer can continue using search and ingest data beyond the license limits, they should be aware that the system is recording violations, which could potentially have consequences depending on the organization’s licensing agreement or usage policies.

Therefore, Answer D is correct because, in this case, the search functionality will not be locked out, even if the data ingestion exceeds the licensed limits, and violations will still be recorded for compliance and auditing purposes.

Question No 6:

In the context of a Search Head Cluster (SHC) in Splunk, which of the following tasks does the Deployer perform? (Select all that apply.)

A. Distributes applications to the members of the Search Head Cluster. 

B. Initializes a fresh installation of Splunk on the Search Head Cluster. 

C. Distributes configuration file changes that are not related to search operations or are manually configured. 

D. Distributes runtime knowledge object changes (such as user-made modifications) across the Search Head Cluster.

Answer:

• A. Distributes applications to SHC members.
  
  

• C. Distributes non-search related and manual configuration file changes.

Explanation:

In a Search Head Cluster (SHC), the Deployer plays a critical role in ensuring that configurations and apps are consistently deployed across all search heads within the cluster. The SHC architecture is used to provide high availability, load balancing, and efficient search operations across multiple Splunk search heads. The Deployer facilitates configuration management and synchronization among these search heads, ensuring a seamless user experience and uniformity across the environment.

A. Distributes applications to SHC members:

The Deployer is responsible for distributing applications to the various search heads in the cluster. This includes both Splunk-provided apps as well as custom apps that may be added to enhance functionality. By ensuring that every member of the SHC has the same applications installed, the Deployer helps maintain consistency and reliability across the cluster. This role is essential for the cluster’s operation, as mismatched apps can lead to performance issues or unexpected behavior.

B. Initializes a fresh installation of Splunk on the SHC:

This option is incorrect. The Deployer does not handle the initial installation or bootstrapping of Splunk on the cluster nodes. Bootstrapping typically involves manual setup or the use of automation tools to prepare the environment, after which the Deployer takes over the configuration and app distribution tasks.

C. Distributes non-search related and manual configuration file changes:

The Deployer also distributes non-search-related configurations such as system settings and configuration files that aren’t directly tied to search operations. These could include server configurations or indexing settings that need to be uniformly applied across the SHC members to ensure smooth functioning.

D. Distributes runtime knowledge object changes:

This is incorrect. Runtime knowledge objects (such as saved searches or user-created alerts) are managed by other components of the Splunk environment. The Deployer is focused on configuration and app deployment, not runtime data or user-specific changes.

In conclusion, the Deployer's role is central to managing and maintaining uniformity in the configuration and app deployment across all search heads in an SHC, ensuring the environment runs smoothly and efficiently.

Question No 7:

When configuring the props.conf file to specify multi-line event delimiters using the LINE_BREAKER attribute in Splunk, 

What value should be assigned to the SHOULD_LINEMERGE attribute?

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

Detailed Question with Answer and Explanation:

Question:

In Splunk, when you are using the props.conf configuration file to define multi-line event delimiters, the LINE_BREAKER attribute is employed to specify how events should be split across multiple lines. To ensure that the events are merged appropriately and that the data is processed as a single event rather than multiple separate ones, it is important to properly configure the SHOULD_LINEMERGE attribute.Which value should the SHOULD_LINEMERGE attribute be set to in this context?

Answer:

The correct answer is C. True.

Explanation:

In Splunk, the props.conf file is used to configure settings related to event processing. One of the primary tasks in event processing is the correct handling of multi-line events, such as log files that span multiple lines for a single event. The LINE_BREAKER attribute is used to define a regular expression that determines where to break an event into separate lines. However, when working with multi-line events, we also need to ensure that these events are properly merged into a single event rather than being mistakenly interpreted as multiple separate events.

To achieve this, the SHOULD_LINEMERGE attribute plays a critical role. This attribute controls whether Splunk should attempt to merge lines that follow each other based on the LINE_BREAKER setting. The value of SHOULD_LINEMERGE can be set to the following:

• True: This is the correct value when you want Splunk to merge subsequent lines of data into a single event. This ensures that events spanning multiple lines are processed as a single, continuous event.
  
  

• False: If set to false, Splunk will not merge the lines, which could result in multiple events being created from a single multi-line entry.
  
  

• Auto: This is not a valid setting for SHOULD_LINEMERGE.
  
  

• None: This setting is not applicable for this attribute.

When working with logs or data sources that contain multi-line events, setting SHOULD_LINEMERGE to True ensures that the entire event is captured in one piece, making the analysis and search more accurate and meaningful.

Question No 8:

When creating a deployment plan for a system or infrastructure, which of the following elements should be included? (Select all that apply.)

A. Business continuity and disaster recovery plans. 

B. Documentation of current logging practices and inventory of data sources.

C. Current and future topology diagrams of the IT environment. 

D. A comprehensive list of stakeholders, both direct and indirect.

Answer:

• A. Business continuity and disaster recovery plans.
  
  

• B. Documentation of current logging practices and inventory of data sources.
  
  

• C. Current and future topology diagrams of the IT environment.
  
  

• D. A comprehensive list of stakeholders, both direct and indirect.

Explanation:

A deployment plan is a critical document for ensuring the successful rollout and operation of a system or IT infrastructure. It serves as a roadmap for the deployment process and addresses all the elements required for a smooth, efficient, and sustainable implementation. Below are key elements that should be included in a deployment plan:

A. Business continuity and disaster recovery plans:

This is essential in any deployment plan. Business continuity ensures that the system can continue functioning in the event of unforeseen disruptions, while disaster recovery outlines procedures for recovering the system after a failure. Including these plans in the deployment helps to minimize downtime, safeguard against data loss, and ensure that the system can quickly return to normal operation.

B. Documentation of current logging practices and inventory of data sources:

A deployment plan should also include details about logging practices and the data sources that will be integrated into the system. This ensures that logging and data management are properly set up from the start and that key data sources are accounted for. These practices are essential for troubleshooting, auditing, and maintaining compliance.

C. Current and future topology diagrams of the IT environment:

Topological diagrams are crucial for visualizing how the infrastructure is laid out, both now and in the future. These diagrams help stakeholders understand how various components are connected and how the system will evolve over time. They are useful for identifying potential bottlenecks, scalability issues, and areas where optimizations may be needed.

D. A comprehensive list of stakeholders:

Identifying all stakeholders—both direct and indirect—helps ensure that the deployment plan addresses the needs and concerns of everyone involved. This includes internal users, IT teams, vendors, and any other parties who have a role in or are impacted by the deployment. Effective stakeholder management can prevent miscommunication and ensure that all requirements are met.

In conclusion, a well-rounded deployment plan includes not only technical details like topology and logging but also considers the business impact and stakeholders, ensuring a comprehensive approach to system implementation and management.

Question No 9:

Which of the following methods can be used to configure a multi-site indexer cluster in Splunk? (Select all that apply.)

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

Answer:

The correct answers are:

A. Using Splunk Web interface.
B. Manually editing the server.conf file located at SPLUNK_HOME/etc/system/local/.
C. Running the splunk edit cluster-config command from the command-line interface (CLI).

Explanation:

Configuring a multi-site indexer cluster in Splunk involves setting up several Splunk instances to work together in a distributed environment. A multi-site indexer cluster typically spans multiple data centers or geographic locations, which requires careful configuration of each Splunk instance to ensure proper data distribution, redundancy, and high availability.

A. Using Splunk Web interface:

Splunk provides a graphical user interface called Splunk Web, where administrators can configure cluster settings, including multi-site indexer cluster configuration. This method is user-friendly and doesn't require manual editing of configuration files. Administrators can define the cluster settings such as the site configuration, replication factor, and search factor directly within the Web UI. This is a preferred option for those who are not comfortable with command-line configuration.

B. Manually editing the server.conf file located at SPLUNK_HOME/etc/system/local/:

Another method to configure the multi-site indexer cluster is by directly editing the server.conf file. The server.conf file is used to configure various Splunk server settings, including those related to clustering. The configuration in SPLUNK_HOME/etc/system/local/ is where custom or override settings are stored. By modifying this file, administrators can specify the site's attributes, such as the site name and the behavior of the indexer cluster.

C. Running the splunk edit cluster-config command from the command-line interface (CLI):

The splunk edit cluster-config command is a powerful CLI option that allows administrators to configure cluster settings directly from the terminal. This method provides more flexibility and is preferred in automated or scripted deployments. Through this command, you can configure aspects like the replication factor, the search factor, and multi-site cluster settings.

D. Directly modifying the server.conf file located at SPLUNK_HOME/etc/system/default/:

This is not recommended because the server.conf file in the default directory is meant to store the default settings provided by Splunk. Modifying these files can lead to issues during upgrades, as changes will be overwritten. Custom configuration settings should always be placed in the local directory to ensure they persist across updates and deployments.

In conclusion, configuring a multi-site indexer cluster in Splunk can be accomplished through the Web interface, the CLI, or by manually editing configuration files, with each method offering varying levels of control and ease of use.

Question No 10:

Which of the following index-time props.conf attributes in Splunk have a direct impact on indexing performance? Select all that apply.

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

Answer:
The correct answers are:

• B. LINE_BREAKER
  
  

• D. SHOULD_LINEMERGE

Explanation:

In Splunk, props.conf is a configuration file used to define how data is parsed and indexed. It includes various attributes that influence how events are processed during indexing, and some of these attributes can directly affect indexing performance. Let’s explore the impact of each of the given attributes:

1. B. LINE_BREAKER:
   The LINE_BREAKER attribute is responsible for determining where one event ends and another begins, particularly when dealing with multiline events. It uses regular expressions to identify line breaks. Since this process can require significant computation, especially for large or complex datasets, it can directly affect indexing performance. If the LINE_BREAKER regex is too complex or inefficient, it can slow down indexing, making it an important factor to consider when optimizing performance.
   
   

2. D. SHOULD_LINEMERGE:
   The SHOULD_LINEMERGE attribute controls whether Splunk attempts to merge events that span multiple lines. Setting this option to true (the default) can help improve event integrity by combining multiline logs (like stack traces or log files) into single events. However, the process of checking for and merging lines can also impact indexing performance, particularly with large amounts of data. If the event boundary detection is complex, it can slow down indexing.
   
   

3. A. REPORT:
   The REPORT attribute is used to reference event transformations, such as field extractions or lookups, that happen during indexing. While field extractions can have an impact on indexing performance, the REPORT attribute itself does not directly control the indexing speed. It is more related to the post-processing of events, affecting search performance rather than indexing performance.
   
   

4. C. ANNOTATE_PUNCT:
   The ANNOTATE_PUNCT attribute allows Splunk to annotate punctuation in events. This setting primarily impacts how the data is parsed and displayed in searches, but it does not significantly affect the indexing performance. It’s more of a data formatting and presentation attribute than an indexing performance factor.

Conclusion:

Attributes like LINE_BREAKER and SHOULD_LINEMERGE have a direct impact on the indexing performance because they influence how events are segmented and processed. Optimizing these attributes can improve indexing efficiency. Meanwhile, REPORT and ANNOTATE_PUNCT have less of a direct impact on indexing speed, though they can affect other aspects of Splunk's data processing.