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Splunk SPLK-5002 Exam Overview:

Certification Vendor:Splunk
Exam Name:Splunk Certified Cybersecurity Defense Engineer
Exam Number:SPLK-5002
Passing Score:65-70% (variable)
Real Exam Qty:82
Available Languages:English
Certificate Validity Period:3 years
Exam Format:Hands-on lab simulation, Multiple choice, Multiple select
Exam Duration:120 minutes
Exam Price:$200 USD
Related Certifications:Splunk Enterprise Security Certified Admin
Splunk SOAR Certified Automation Developer
Splunk Core Certified User
Sample Questions:Splunk SPLK-5002 Sample Questions
Exam Way:Online proctored exam at Pearson VUE testing centers or remote proctoring
Pre Condition:Splunk Core Certified User, Splunk Enterprise Security Certified Admin, and Splunk SOAR Certified Automation Developer recommended; minimum 1-2 years hands-on Splunk security experience strongly advised
Official Syllabus URL:https://www.splunk.com/en_us/training/certification-track/splunk-certified-cybersecurity-defense-engineer.html

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Splunk SPLK-5002 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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.
Topic 5
  • 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.

Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which of the following actions improve data indexing performance in Splunk?(Choosetwo)

Answer: C,D

Explanation:
How to Improve Data Indexing Performance in Splunk?
Optimizing indexing performance is critical for ensuring faster search speeds, better storage efficiency, and reduced latency in a Splunk deployment.
#Why is "Configuring Index-Time Field Extractions" Important? (Answer B) Extracting fields at index time reduces the need for search-time processing, making searches faster.
Example: If security logs contain IP addresses, usernames, or error codes, configuring index-time extraction ensures that these fields are already available during searches.
#Why "Increasing the Number of Indexers in a Distributed Environment" Helps? (Answer D) Adding more indexers distributes the data load, improving overall indexing speed and search performance.
Example: In a large SOC environment, more indexers allow for faster log ingestion from multiple sources (firewalls, IDS, cloud services).
Why Not the Other Options?
#A. Indexing data with detailed metadata - Adding too much metadata increases indexing overhead and slows down performance.#C. Using lightweight forwarders for data ingestion - Lightweight forwarders only forward raw data and don't enhance indexing performance.
References & Learning Resources
#Splunk Indexing Performance Guide: https://docs.splunk.com/Documentation/Splunk/latest/Indexer
/Howindexingworks#Best Practices for Splunk Indexing Optimization: https://splunkbase.splunk.
com#Distributed Splunk Architecture for Large-Scale Environments: https://www.splunk.com/en_us/blog
/tips-and-tricks


NEW QUESTION # 16
An engineer receives a report that the "Traffic over time by action" dashboard is not populating. It has been confirmed that the relevant logs are being ingested properly and they are CIM compliant. What other configuration may be missing?

Answer: A

Explanation:
The "Traffic over time by action" dashboard relies on the Network Traffic data model. For it to populate correctly, the data model must be accelerated, ensuring that the dashboard can pull from the accelerated summaries instead of raw data.


NEW QUESTION # 17
Which Splunk feature helps to standardize data for better search accuracy and detection logic?

Answer: B

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 # 18
Which of the following should an engineer do as they evaluate their Threat Detection and Incident Response lifecycle?

Answer: B

Explanation:
An engineer should evaluate the threat process lifecycle based on contextual business and industry knowledge. This ensures that detection and response efforts are aligned with the threats most relevant to the organization's environment, industry risks, and business priorities.


NEW QUESTION # 19
A security analyst needs to update the SOP for handling phishing incidents.
What should they prioritize?

Answer: C

Explanation:
Updating the SOP for Handling Phishing Incidents
AStandard Operating Procedure (SOP)should focus onprevention, detection, and response.
#1. Documenting Steps for User Awareness Training (C)
Training employeeshelps prevent phishing incidents.
Example:
Teach users toidentify phishing emails and report them via a Splunk SOAR playbook.
#Incorrect Answers:
A: Ensuring all reports are manually verified by analysts#Automation(via SOAR) should be used forinitial triage.
B: Automating the isolation of suspected phishing emails# Automation is useful, butuser education prevents incidents.
D: Reporting incidents to the executive board immediately#Only major security breachesshould beescalated to executives.
#Additional Resources:
NIST Incident Response Guide
Splunk Phishing Detection Playbooks


NEW QUESTION # 20
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