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The SPLK-2002 exam is a certification test designed to evaluate the knowledge and skills of candidates seeking to become certified Splunk Enterprise architects. Splunk Enterprise Certified Architect certification is considered to be a top-tier qualification that validates the ability of professionals to design and implement complex Splunk Enterprise environments. SPLK-2002 exam tests the candidate's ability to design, deploy, and manage complex Splunk environments that can handle large amounts of data.
Splunk SPLK-2002 Certification Exam tests the candidate's knowledge of various aspects of Splunk Enterprise, including data inputs, data parsing and manipulation, data visualization, and data management. SPLK-2002 exam also covers topics such as scalability, data security, and troubleshooting. Candidates are also evaluated on their ability to design and implement Splunk architecture that meets the needs of the organization. Splunk Enterprise Certified Architect Sample Questions (Q100-Q105):NEW QUESTION # 100
When using the props.conf LINE_BREAKER attribute to delimit multi-line events, the SHOULD_LINEMERGE attribute should be set to what?
A. False
B. None
C. True
D. Auto
Answer: A
Explanation:
When using the props.conf LINE_BREAKER attribute to delimit multi-line events, the SHOULD_LINEMERGE attribute should be set to false. This tells Splunk not to merge events that have been broken by the LINE_BREAKER. Setting the SHOULD_LINEMERGE attribute to true, auto, or none will cause Splunk to ignore the LINE_BREAKER and merge events based on other criteria. For more information, see Configure event line breaking in the Splunk documentation.
NEW QUESTION # 101
The KV store forms its own cluster within a SHC. What is the maximum number of SHC members KV store will form?
A. 0
B. 1
C. 2
D. Unlimited
Answer: D
NEW QUESTION # 102
(A high-volume source and a low-volume source feed into the same index. Which of the following items best describe the impact of this design choice?)
A. Search speed on low volume data will be slower than necessary.
B. Low volume data will improve the compression factor of the high volume data.
C. High volume data is optimized by the presence of low volume data.
D. Low volume data may move out of the index based on volume rather than age.
Answer: A,D
Explanation:
The Splunk Managing Indexes and Storage Documentation explains that when multiple data sources with significantly different ingestion rates share a single index, index bucket management is governed by volume- based rotation, not by source or time. This means that high-volume data causes buckets to fill and roll more quickly, which in turn causes low-volume data to age out prematurely, even if it is relatively recent - hence Option C is correct.
Additionally, because Splunk organizes data within index buckets based on event time and storage characteristics, low-volume data mixed with high-volume data results in inefficient searches for smaller datasets. Queries that target the low-volume source will have to scan through the same large number of buckets containing the high-volume data, leading to slower-than-necessary search performance - Option B.
Compression efficiency (Option A) and performance optimization through data mixing (Option D) are not influenced by mixing volume patterns; these are determined by the event structure and compression algorithm, not source diversity. Splunk best practices recommend separating data sources into different indexes based on usage, volume, and retention requirements to optimize both performance and lifecycle management.
References (Splunk Enterprise Documentation):
* Managing Indexes and Storage - How Splunk Manages Buckets and Data Aging
* Splunk Indexing Performance and Data Organization Best Practices
* Splunk Enterprise Architecture and Data Lifecycle Management
* Best Practices for Data Volume Segregation and Retention Policies
NEW QUESTION # 103
A new Splunk customer is using syslog to collect data from their network devices on port 514. What is the best practice for ingesting this data into Splunk?
A. Use a Splunk indexer to collect a network input on port 514 directly.
B. Configure syslog to send the data to multiple Splunk indexers.
C. Configure syslog to write logs and use a Splunk forwarder to collect the logs.
D. Use a Splunk forwarder to collect the input on port 514 and forward the data.
Answer: C
Explanation:
The best practice for ingesting syslog data from network devices on port 514 into Splunk is to configure syslog to write logs and use a Splunk forwarder to collect the logs. This practice will ensure that the data is reliably collected and forwarded to Splunk, without losing any data or overloading the Splunk indexer.
Configuring syslog to send the data to multiple Splunk indexers will not guarantee data reliability, as syslog is a UDP protocol that does not provide acknowledgment or delivery confirmation. Using a Splunk indexer to collect a network input on port 514 directly will not provide data reliability or load balancing, as the indexer may not be able to handle the incoming data volume or distribute it to other indexers. Using a Splunk forwarder to collect the input on port 514 and forward the data will not provide data reliability, as the forwarder may not be able to receive the data from syslog or buffer it in case of network issues. For more information, see [Get data from TCP and UDP ports] and [Best practices for syslog data] in the Splunk documentation.
NEW QUESTION # 104
Splunk Enterprise platform instrumentation refers to data that the Splunk Enterprise deployment logs in the
_introspection index. Which of the following logs are included in this index? (Select all that apply.)
A. audit.log
B. metrics.log
C. resource_usage.log
D. disk_objects.log
Answer: C,D
Explanation:
The following logs are included in the _introspection index, which contains data that the Splunk Enterprise deployment logs for platform instrumentation:
* disk_objects.log. This log contains information about the disk objects that Splunk creates and manages, such as buckets, indexes, and files. This log can help monitor the disk space usage and the bucket lifecycle.
* resource_usage.log. This log contains information about the resource usage of Splunk processes, such as CPU, memory, disk, and network. This log can help monitor the Splunk performance and identify any resource bottlenecks. The following logs are not included in the _introspection index, but rather in the
_internal index, which contains data that Splunk generates for internal logging:
* audit.log. This log contains information about the audit events that Splunk records, such as user actions, configuration changes, and search activity. This log can help audit the Splunk operations and security.
* metrics.log. This log contains information about the performance metrics that Splunk collects, such as data throughput, data latency, search concurrency, and search duration. This log can help measure the Splunk performance and efficiency. For more information, see About Splunk Enterprise logging and
[About the _introspection index] in the Splunk documentation.
NEW QUESTION # 105
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