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SPLK-5002 Real Sheets - Valid SPLK-5002 Test Book

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q37-Q42):

NEW QUESTION # 37
A company wants to create a dashboard that displays normalized event data from various sources.
Whatapproach should they use?

A. Apply search-time field extractions.
B. Implement a data model using CIM.
C. Use SPL queries to manually extract fields.
D. Configure a summary index.

Answer: B

Explanation:
When organizations need to normalize event data from various sources, using Common Information Model (CIM) in Splunk is the best approach.
Why Use CIM for Normalized Event Data?
Standardizes Data Across Different Log Sources
CIM ensures consistent field names and formats across varied log types.
Makes searches, reports, and dashboards easier to manage.
Enables Faster and More Efficient Searches
Uses Data Models to accelerate search queries.
Reduces the need for custom field extractions.

NEW QUESTION # 38
What is the purpose of leveraging REST APIs in a Splunk automation workflow?

A. To compress data before indexing
B. To configure storage retention policies
C. To generate predefined reports
D. To integrate Splunk with external applications and automate interactions

Answer: D

Explanation:
Splunk's REST API allows external applications and security tools to automate workflows, integrate with Splunk, and retrieve/search data programmatically.
#Why Use REST APIs in Splunk Automation?
Automates interactions between Splunk and other security tools.
Enables real-time data ingestion, enrichment, and response actions.
Used in Splunk SOAR playbooks for automated threat response.
Example:
A security event detected in Splunk ES triggers a Splunk SOAR playbook via REST API to:
Retrieve threat intelligence from VirusTotal.
Block the malicious IP in Palo Alto firewall.
Create an incident ticket in ServiceNow.
#Incorrect Answers:
A: To configure storage retention policies # Storage is managed via Splunk indexing, not REST APIs.
C: To compress data before indexing # Splunk does not use REST APIs for data compression.
D: To generate predefined reports # Reports are generated using Splunk's search and reporting functionality, not APIs.
#Additional Resources:
Splunk REST API Documentation
Automating Workflows with Splunk API

NEW QUESTION # 39
What are the key components of Splunk's indexing process?(Choosethree)

A. Input phase
B. Parsing
C. Alerting
D. Indexing
E. Searching

Answer: A,B,D

Explanation:
Key Components of Splunk's Indexing Process
Splunk's indexing process consists of multiple stages that ingest, process, and store data efficiently for search and analysis.
#1. Input Phase (E)
Collects data from sources (e.g., syslogs, cloud services, network devices).
Defines where the data comes from and applies pre-processing rules.
Example:
A firewall log is ingested from a syslog server into Splunk.
#2. Parsing (A)
Breaks raw data into individual events.
Applies rules for timestamp extraction, line breaking, and event formatting.
Example:
A multiline log file is parsed so that each log entry is a separate event.
#3. Indexing (C)
Stores parsed data in indexes to enable fast searching.
Assigns metadata like host, source, and sourcetype.
Example:
An index=firewall_logs contains all firewall-related events.
#Incorrect Answers:
B: Searching # Searching happens after indexing, not during the indexing process.
D: Alerting # Alerting is part of SIEM and detection, not indexing.
#Additional Resources:
Splunk Indexing Process Documentation
Splunk Data Processing Pipeline

NEW QUESTION # 40
What Splunk process ensures that duplicate data is not indexed?

A. Metadata tagging
B. Data deduplication
C. Event parsing
D. Indexer clustering

Answer: C

Explanation:
Splunk prevents duplicate data from being indexed through event parsing, which occurs during the data ingestion process.
How Event Parsing Prevents Duplicate Data:
Splunk's indexer parses incoming data and assigns unique timestamps, metadata, and event IDs to prevent reindexing duplicate logs.
CRC Checks (Cyclic Redundancy Checks) are applied to avoid duplicate event ingestion.
Index-time filtering and transformation rules help detect and drop repeated data before indexing.

NEW QUESTION # 41
During a high-priority incident, a user queries an index but sees incomplete results.
Whatis the most likely issue?

A. Indexers have reached their queue capacity.
B. Buckets in the warm state are inaccessible.
C. The search head configuration is outdated.
D. Data normalization was not applied.

Answer: A

Explanation:
If a user queries an index during a high-priority incident but sees incomplete results, it is likely that the indexers are overloaded, causing queue bottlenecks.
Why Indexer Queue Capacity Issues Cause Incomplete Results:
When indexing queues fill up, incoming data cannot be processed efficiently.
Search results may be incomplete or delayed if events are still in the indexing queue and not fully written to disk.
Heavy search loads during incidents can also increase pressure on indexers.
How to Fix It:
Monitor indexing queues via the Monitoring Console (indexing>indexing performance).
Checkmetrics.logon indexers formax_queue_size_exceededwarnings.
Increase indexer capacity or optimize search scheduling to reduce load.

NEW QUESTION # 42
......

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