Practice Free SPLK-2002 Exam Online Questions
Question #31
Which Splunk tool offers a health check for administrators to evaluate the health of their Splunk deployment?
- A . btool
- B . DiagGen
- C . SPL Clinic
- D . Monitoring Console
Correct Answer: D
D
Explanation:
The Monitoring Console is the Splunk tool that offers a health check for administrators to evaluate the health of their Splunk deployment. The Monitoring Console provides dashboards and alerts that show the status and performance of various Splunk components, such as indexers, search heads, forwarders, license usage, and search activity. The Monitoring Console can also run health checks on the deployment and identify any issues or recommendations. The btool is a command-line tool that shows the effective settings of the configuration files, but it does not offer a health check. The DiagGen is a tool that generates diagnostic snapshots of the Splunk environment, but it does not offer a health check. The SPL Clinic is a tool that analyzes and optimizes SPL queries, but it does not offer a health check. For more information, see About the Monitoring Console in the Splunk documentation.
D
Explanation:
The Monitoring Console is the Splunk tool that offers a health check for administrators to evaluate the health of their Splunk deployment. The Monitoring Console provides dashboards and alerts that show the status and performance of various Splunk components, such as indexers, search heads, forwarders, license usage, and search activity. The Monitoring Console can also run health checks on the deployment and identify any issues or recommendations. The btool is a command-line tool that shows the effective settings of the configuration files, but it does not offer a health check. The DiagGen is a tool that generates diagnostic snapshots of the Splunk environment, but it does not offer a health check. The SPL Clinic is a tool that analyzes and optimizes SPL queries, but it does not offer a health check. For more information, see About the Monitoring Console in the Splunk documentation.
Question #32
Which of the following will cause the greatest reduction in disk size 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.
Correct Answer: C
C
Explanation:
Decreasing the data model acceleration range will reduce the disk size requirements for a cluster of indexers running Splunk Enterprise Security. Data model acceleration creates tsidx files that consume disk space on the indexers. Reducing the acceleration range will limit the amount of data that is accelerated and thus save disk space. Setting the cluster search factor or replication factor to N-1 will not reduce the disk size requirements, but rather increase the risk of data loss. Increasing the number of buckets per index will also increase the disk size requirements, as each bucket has a minimum size. For more information, see Data model acceleration and Bucket size in the Splunk documentation.
C
Explanation:
Decreasing the data model acceleration range will reduce the disk size requirements for a cluster of indexers running Splunk Enterprise Security. Data model acceleration creates tsidx files that consume disk space on the indexers. Reducing the acceleration range will limit the amount of data that is accelerated and thus save disk space. Setting the cluster search factor or replication factor to N-1 will not reduce the disk size requirements, but rather increase the risk of data loss. Increasing the number of buckets per index will also increase the disk size requirements, as each bucket has a minimum size. For more information, see Data model acceleration and Bucket size in the Splunk documentation.
Question #33
The guidance Splunk gives for estimating size on for syslog data is 50% of original data size.
How does this divide between files in the index?
- A . rawdata is: 10%, tsidx is: 40%
- B . rawdata is: 15%, tsidx is: 35%
- C . rawdata is: 35%, tsidx is: 15%
- D . rawdata is: 40%, tsidx is: 10%
Correct Answer: B
B
Explanation:
The guidance Splunk gives for estimating size on for syslog data is 50% of original data size. This divides between files in the index as follows: rawdata is 15%, tsidx is 35%. The rawdata is the compressed version of the original data, which typically takes about 15% of the original data size. The tsidx is the index file that contains the time-series metadata and the inverted index, which typically takes about 35% of the original data size. The total size of the rawdata and the tsidx is about 50% of the original data size. For more information, see [Estimate your storage requirements] in the Splunk documentation.
B
Explanation:
The guidance Splunk gives for estimating size on for syslog data is 50% of original data size. This divides between files in the index as follows: rawdata is 15%, tsidx is 35%. The rawdata is the compressed version of the original data, which typically takes about 15% of the original data size. The tsidx is the index file that contains the time-series metadata and the inverted index, which typically takes about 35% of the original data size. The total size of the rawdata and the tsidx is about 50% of the original data size. For more information, see [Estimate your storage requirements] in the Splunk documentation.
Question #34
Which of the following statements about integrating with third-party systems is true? (Select all that apply.)
- A . A Hadoop application can search data in Splunk.
- B . Splunk can search data in the Hadoop File System (HDFS).
- C . You can use Splunk alerts to provision actions on a third-party system.
- D . You can forward data from Splunk forwarder to a third-party system without indexing it first.
Correct Answer: C,D
C,D
Explanation:
The following statements about integrating with third-party systems are true: You can use Splunk alerts to provision actions on a third-party system, and you can forward data from Splunk forwarder to a third-party system without indexing it first. Splunk alerts are triggered events that can execute custom actions, such as sending an email, running a script, or calling a webhook. Splunk alerts can be used to integrate with third-party systems, such as ticketing systems, notification services, or automation platforms. For example, you can use Splunk alerts to create a ticket in ServiceNow, send a message to Slack, or trigger a workflow in Ansible. Splunk forwarders are Splunk instances that collect and forward data to other Splunk instances, such as indexers or heavy forwarders. Splunk forwarders can also forward data to third-party systems, such as Hadoop, Kafka, or AWS Kinesis, without indexing it first. This can be useful for sending data to other data processing or storage systems, or for integrating with other analytics or monitoring tools. A Hadoop application cannot search data in Splunk, because Splunk does not provide a native interface for Hadoop applications to access Splunk data. Splunk can search data in the Hadoop File System (HDFS), but only by using the Hadoop Connect app, which is a Splunk app that enables Splunk to index and search data stored in HDFS
C,D
Explanation:
The following statements about integrating with third-party systems are true: You can use Splunk alerts to provision actions on a third-party system, and you can forward data from Splunk forwarder to a third-party system without indexing it first. Splunk alerts are triggered events that can execute custom actions, such as sending an email, running a script, or calling a webhook. Splunk alerts can be used to integrate with third-party systems, such as ticketing systems, notification services, or automation platforms. For example, you can use Splunk alerts to create a ticket in ServiceNow, send a message to Slack, or trigger a workflow in Ansible. Splunk forwarders are Splunk instances that collect and forward data to other Splunk instances, such as indexers or heavy forwarders. Splunk forwarders can also forward data to third-party systems, such as Hadoop, Kafka, or AWS Kinesis, without indexing it first. This can be useful for sending data to other data processing or storage systems, or for integrating with other analytics or monitoring tools. A Hadoop application cannot search data in Splunk, because Splunk does not provide a native interface for Hadoop applications to access Splunk data. Splunk can search data in the Hadoop File System (HDFS), but only by using the Hadoop Connect app, which is a Splunk app that enables Splunk to index and search data stored in HDFS
Question #35
Which of the following options in limits, conf may provide performance benefits at the forwarding tier?
- A . Enable the indexed_realtime_use_by_default attribute.
- B . Increase the maxKBps attribute.
- C . Increase the parallellngestionPipelines attribute.
- D . Increase the max_searches per_cpu attribute.
Correct Answer: C
C
Explanation:
The correct answer is
C. Increase the parallellngestionPipelines attribute. This is an option in limits.conf that may provide performance benefits at the forwarding tier, as it allows the forwarder to process multiple data inputs in parallel1. The parallellngestionPipelines attribute specifies the number of pipelines that the forwarder can use to ingest data from different sources1. By increasing this value, the forwarder can improve its throughput and reduce the latency of data delivery1. The other options are not effective options to provide performance benefits at the forwarding tier.
Option A, enabling the indexed_realtime_use_by_default attribute, is not recommended, as it enables the forwarder to send data to the indexer as soon as it is received, which may increase the network and CPU load and degrade the performance2.
Option B, increasing the maxKBps attribute, is
not a good option, as it increases the maximum bandwidth, in kilobytes per second, that the forwarder can use to send data to the indexer3. This may improve the data transfer speed, but it may also saturate the network and cause congestion and packet loss3.
Option D, increasing the
max_searches_per_cpu attribute, is not relevant, as it only affects the search performance on the indexer or search head, not the forwarding performance on the forwarder4. Therefore, option C is the correct answer, and options A, B, and D are incorrect.
1: Configure parallel ingestion pipelines
2: Configure real-time forwarding
3: Configure forwarder output
4: Configure search performance
C
Explanation:
The correct answer is
C. Increase the parallellngestionPipelines attribute. This is an option in limits.conf that may provide performance benefits at the forwarding tier, as it allows the forwarder to process multiple data inputs in parallel1. The parallellngestionPipelines attribute specifies the number of pipelines that the forwarder can use to ingest data from different sources1. By increasing this value, the forwarder can improve its throughput and reduce the latency of data delivery1. The other options are not effective options to provide performance benefits at the forwarding tier.
Option A, enabling the indexed_realtime_use_by_default attribute, is not recommended, as it enables the forwarder to send data to the indexer as soon as it is received, which may increase the network and CPU load and degrade the performance2.
Option B, increasing the maxKBps attribute, is
not a good option, as it increases the maximum bandwidth, in kilobytes per second, that the forwarder can use to send data to the indexer3. This may improve the data transfer speed, but it may also saturate the network and cause congestion and packet loss3.
Option D, increasing the
max_searches_per_cpu attribute, is not relevant, as it only affects the search performance on the indexer or search head, not the forwarding performance on the forwarder4. Therefore, option C is the correct answer, and options A, B, and D are incorrect.
1: Configure parallel ingestion pipelines
2: Configure real-time forwarding
3: Configure forwarder output
4: Configure search performance
Question #36
When should multiple search pipelines be enabled?
- A . Only if disk IOPS is at 800 or better.
- B . Only if there are fewer than twelve concurrent users.
- C . Only if running Splunk Enterprise version 6.6 or later.
- D . Only if CPU and memory resources are significantly under-utilized.
Correct Answer: D
D
Explanation:
Multiple search pipelines should be enabled only if CPU and memory resources are significantly under-utilized. Search pipelines are the processes that execute search commands and return results. Multiple search pipelines can improve the search performance by running concurrent searches in parallel. However, multiple search pipelines also consume more CPU and memory resources, which can affect the overall system performance. Therefore, multiple search pipelines should be enabled only if there are enough CPU and memory resources available, and if the system is not bottlenecked by disk I/O or network bandwidth. The number of concurrent users, the disk IOPS, and the Splunk Enterprise version are not relevant factors for enabling multiple search pipelines
D
Explanation:
Multiple search pipelines should be enabled only if CPU and memory resources are significantly under-utilized. Search pipelines are the processes that execute search commands and return results. Multiple search pipelines can improve the search performance by running concurrent searches in parallel. However, multiple search pipelines also consume more CPU and memory resources, which can affect the overall system performance. Therefore, multiple search pipelines should be enabled only if there are enough CPU and memory resources available, and if the system is not bottlenecked by disk I/O or network bandwidth. The number of concurrent users, the disk IOPS, and the Splunk Enterprise version are not relevant factors for enabling multiple search pipelines
Question #37
Which of the following statements describe search head clustering? (Select all that apply.)
- A . A deployer is required.
- B . At least three search heads are needed.
- C . Search heads must meet the high-performance reference server requirements.
- D . The deployer must have sufficient CPU and network resources to process service requests and push configurations.
Correct Answer: A,B,D
A,B,D
Explanation:
Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching.
Search head clustering has the following characteristics:
A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.
At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.
The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.
Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.
A,B,D
Explanation:
Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching.
Search head clustering has the following characteristics:
A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.
At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.
The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.
Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.
Question #38
Following Splunk recommendations, where could the Monitoring Console (MC) be installed in a distributed deployment with an indexer cluster, a search head cluster, and 1000 forwarders?
- A . On a search peer in the cluster.
- B . On the deployment server.
- C . On the search head cluster deployer.
- D . On a search head in the cluster.
Correct Answer: C
C
Explanation:
The Monitoring Console (MC) is the Splunk Enterprise monitoring tool that lets you view detailed topology and performance information about your Splunk Enterprise deployment1. The MC can be installed on any Splunk Enterprise instance that can access the data from all the instances in the deployment2. However, following the Splunk recommendations, the MC should be installed on the search head cluster deployer, which is a dedicated instance that manages the configuration bundle for the search head cluster members3. This way, the MC can monitor the search head cluster as well as the indexer cluster and the forwarders, without affecting the performance or availability of the other instances4. The other options are not recommended because they either introduce additional load on the existing instances (such as A and D) or do not have access to the data from the search head cluster (such as B).
1: About the Monitoring Console – Splunk Documentation
2: Add Splunk Enterprise instances to the Monitoring Console
3: Configure the deployer – Splunk Documentation
4: [Monitoring Console setup and use – Splunk Documentation]
C
Explanation:
The Monitoring Console (MC) is the Splunk Enterprise monitoring tool that lets you view detailed topology and performance information about your Splunk Enterprise deployment1. The MC can be installed on any Splunk Enterprise instance that can access the data from all the instances in the deployment2. However, following the Splunk recommendations, the MC should be installed on the search head cluster deployer, which is a dedicated instance that manages the configuration bundle for the search head cluster members3. This way, the MC can monitor the search head cluster as well as the indexer cluster and the forwarders, without affecting the performance or availability of the other instances4. The other options are not recommended because they either introduce additional load on the existing instances (such as A and D) or do not have access to the data from the search head cluster (such as B).
1: About the Monitoring Console – Splunk Documentation
2: Add Splunk Enterprise instances to the Monitoring Console
3: Configure the deployer – Splunk Documentation
4: [Monitoring Console setup and use – Splunk Documentation]
Question #39
(The performance of a specific search is performing poorly. The search must run over All Time and is expected to have very few results. Analysis shows that the search accesses a very large number of buckets in a large index.
What step would most significantly improve the performance of this search?)
- A . Increase the disk I/O hardware performance.
- B . Increase the number of indexing pipelines.
- C . Set indexed_realtime_use_by_default = true in limits.conf.
- D . Change this to a real-time search using an All Time window.
Correct Answer: A
A
Explanation:
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.
Reference (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
A
Explanation:
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.
Reference (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
Question #40
When Splunk is installed, where are the internal indexes stored by default?
- A . SPLUNK_HOME/bin
- B . SPLUNK_HOME/var/lib
- C . SPLUNK_HOME/var/run
- D . SPLUNK_HOME/etc/system/default
Correct Answer: B
B
Explanation:
Splunk internal indexes are the indexes that store Splunk’s own data, such as internal logs, metrics, audit events, and configuration snapshots. By default, Splunk internal indexes are stored in the SPLUNK_HOME/var/lib/splunk directory, along with other user-defined indexes. The SPLUNK_HOME/bin directory contains the Splunk executable files and scripts. The SPLUNK_HOME/var/run directory contains the Splunk process ID files and lock files. The SPLUNK_HOME/etc/system/default directory contains the default Splunk configuration files.
B
Explanation:
Splunk internal indexes are the indexes that store Splunk’s own data, such as internal logs, metrics, audit events, and configuration snapshots. By default, Splunk internal indexes are stored in the SPLUNK_HOME/var/lib/splunk directory, along with other user-defined indexes. The SPLUNK_HOME/bin directory contains the Splunk executable files and scripts. The SPLUNK_HOME/var/run directory contains the Splunk process ID files and lock files. The SPLUNK_HOME/etc/system/default directory contains the default Splunk configuration files.