Free PDF Splunk - SPLK-5002–Trustable Practice Test

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Splunk SPLK-5002 Exam Syllabus Topics:

Topic	Details
Topic 1	Detection Engineering: This section evaluates the expertise of Threat Hunters and SOC Engineers in developing and refining security detections. Topics include creating and tuning correlation searches, integrating contextual data into detections, applying risk-based modifiers, generating actionable Notable Events, and managing the lifecycle of detection rules to adapt to evolving threats.
Topic 2	Auditing and Reporting on Security Programs: This section tests Auditors and Security Architects on validating and communicating program effectiveness. It includes designing security metrics, generating compliance reports, and building dashboards to visualize program performance and vulnerabilities for stakeholders.
Topic 3	Building Effective Security Processes and Programs: This section targets Security Program Managers and Compliance Officers, focusing on operationalizing security workflows. It involves researching and integrating threat intelligence, applying risk and detection prioritization methodologies, and developing documentation or standard operating procedures (SOPs) to maintain robust security practices.
Topic 4	Automation and Efficiency: This section assesses Automation Engineers and SOAR Specialists in streamlining security operations. It covers developing automation for SOPs, optimizing case management workflows, utilizing REST APIs, designing SOAR playbooks for response automation, and evaluating integrations between Splunk Enterprise Security and SOAR tools.
Topic 5	Data Engineering: This section of the exam measures the skills of Security Analysts and Cybersecurity Engineers and covers foundational data management tasks. It includes performing data review and analysis, creating and maintaining efficient data indexing, and applying Splunk methods for data normalization to ensure structured and usable datasets for security operations.

Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which Splunk feature helps to standardize data for better search accuracy and detection logic?

A. Data Models
B. Normalization Rules
C. Event Correlation
D. Field Extraction

Answer: A

Explanation:
Why Use "Data Models" for Standardized Search Accuracy and Detection Logic?
SplunkData Modelsprovide astructured, normalized representationof raw logs, improving:
#Search consistency across different log sources#Detection logic by ensuring standardized field names#Faster and more efficient querieswith data model acceleration
#Example in Splunk Enterprise Security:#Scenario:A SOC team monitors login failures acrossmultiple authentication systems.#Without Data Models:Different logs usesrc_ip, source_ip, or ip_address, making searches complex.#With Data Models:All fieldsmap to a standard format, enablingconsistent detection logic.
Why Not the Other Options?
#A. Field Extraction- Extracts fields from raw events butdoes not standardize field names across sources.#C.
Event Correlation- Detects relationships between logsbut doesn't normalize data for search accuracy.#D.
Normalization Rules- A general term; Splunkuses CIM & Data Models for normalization.
References & Learning Resources
#Splunk Data Models Documentation: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge
/Aboutdatamodels#Using CIM & Data Models for Security Analytics: https://splunkbase.splunk.com/app
/263#How Data Models Improve Search Performance: https://www.splunk.com/en_us/blog/tips-and-

NEW QUESTION # 13
An organization uses MITRE ATT&CK to enhance its threat detection capabilities.
Howshould this methodology be incorporated?

A. Use it only for reporting after incidents.
B. Deploy it as a replacement for current detection systems.
C. Rely solely on vendor-provided threat intelligence.
D. Develop custom detection rules based on attack techniques.

Answer: D

Explanation:
MITRE ATT&CK is a threat intelligence framework that helps security teams map attack techniques to detection rules.
#1. Develop Custom Detection Rules Based on Attack Techniques (A)
Maps Splunk correlation searches to MITRE ATT&CK techniques to detect adversary behaviors.
Example:
To detect T1078 (Valid Accounts):
index=auth_logs action=failed | stats count by user, src_ip
If an account logs in from anomalous locations, trigger an alert.
#Incorrect Answers:
B: Use it only for reporting after incidents # MITRE ATT&CK should be used proactively for threat detection.
C: Rely solely on vendor-provided threat intelligence # Custom rules tailored to an organization's threat landscape are more effective.
D: Deploy it as a replacement for current detection systems # MITRE ATT&CK complements existing SIEM
/EDR tools, not replaces them.
#Additional Resources:
MITRE ATT&CK & Splunk
Using MITRE ATT&CK in SIEMs

NEW QUESTION # 14
An engineer observes a delay in data being indexed from a remote location. The universal forwarder is configured correctly.
Whatshould they check next?

A. Reconfigure the props.conf file.
B. Review forwarder logs for queue blockages.
C. Optimize search head clustering.
D. Increase the indexer memory allocation.

Answer: B

Explanation:
If there is a delay in data being indexed from a remote location, even though the Universal Forwarder (UF) is correctly configured, the issue is likely a queue blockage or network latency.
Steps to Diagnose and Fix Forwarder Delays:
Check Forwarder Logs (splunkd.log) for Queue Issues (A)
Look for messages likeTcpOutAutoLoadBalancedorQueue is full.
If queues are full, events are stuck at the forwarder and not reaching the indexer.
Monitor Forwarder Health Usingmetrics.log
Useindex=_internal source=*metrics.log* group=queueto check queue performance.

NEW QUESTION # 15
What are the key components of Splunk's indexing process?(Choosethree)

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

Answer: A,D,E

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 # 16
What is the primary purpose of developing security metrics in a Splunk environment?

A. To measure and evaluate the effectiveness of security programs
B. To automate case management workflows
C. To enhance data retention policies
D. To identify low-priority alerts for suppression

Answer: A

Explanation:
Security metrics help organizations assess their security posture and make data-driven decisions.
Primary Purpose of Security Metrics in Splunk:
Measure Security Effectiveness (B)
Tracks incident response times, threat detection rates, and alert accuracy.
Helps SOC teams and leadership evaluate security program performance.
Improve Threat Detection & Incident Response
Identifies gaps in detection logic and false positives.
Helps fine-tune correlation searches and notable events.

NEW QUESTION # 17
......

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SPLK-5002 Latest Exam Answers: https://www.actual4cert.com/SPLK-5002-real-questions.html

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FREE PDF SPLUNK - SPLK-5002–TRUSTABLE PRACTICE TEST

Free PDF Splunk - SPLK-5002–Trustable Practice Test