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Splunk SPLK-5002 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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	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.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q17-Q22):

NEW QUESTION # 17
What is the primary purpose of correlation searches in Splunk?

A. To store pre-aggregated search results
B. To extract and index raw data
C. To identify patterns and relationships between multiple data sources
D. To create dashboards for real-time monitoring

Answer: C

Explanation:
Correlation searches in Splunk Enterprise Security (ES) are a critical component of Security Operations Center (SOC) workflows, designed to detect threats by analyzing security data from multiple sources.
Primary Purpose of Correlation Searches:
Identify threats and anomalies: They detect patterns and suspicious activity by correlating logs, alerts, and events from different sources.
Automate security monitoring: By continuously running searches on ingested data, correlationsearches help reduce manual efforts for SOC analysts.
Generate notable events: When a correlation search identifies a security risk, it creates a notable event in Splunk ES for investigation.
Trigger security automation: In combination with Splunk SOAR, correlation searches can initiate automated response actions, such as isolating endpoints or blocking malicious IPs.
Since correlation searches analyze relationships and patterns across multiple data sources to detect security threats, the correct answer is B. To identify patterns and relationships between multiple data sources.
References:
Splunk ES Correlation Searches Overview
Best Practices for Correlation Searches
Splunk ES Use Cases and Notable Events

NEW QUESTION # 18
What methods improve risk and detection prioritization?(Choosethree)

A. Automating detection tuning
B. Enforcing strict search head resource limits
C. Using predefined alert templates
D. Incorporating business context into decisions
E. Assigning risk scores to assets and events

Answer: A,D,E

Explanation:
Risk and detection prioritization in Splunk Enterprise Security (ES) helps SOC analysts focus on the most critical threats. By assigning risk scores, integrating business context, and automating detection tuning, organizations can prioritize security incidents efficiently.
Methods to Improve Risk and Detection Prioritization:
Assigning Risk Scores to Assets and Events (A)
Uses Risk-Based Alerting (RBA) to prioritize high-risk activities based on behavior and history.
Helps SOC teams focus on true threats instead of isolated events.
Incorporating Business Context into Decisions (C)
Adds context from asset criticality, user roles, and business impact.
Ensures alerts are ranked based on their potential business impact.
Automating Detection Tuning (D)
Uses machine learning and adaptive response actions to reduce false positives.
Dynamically adjusts alert thresholds based on evolving threat patterns.

NEW QUESTION # 19
Which of the following actions improve data indexing performance in Splunk?(Choosetwo)

A. Indexing data with detailed metadata
B. Increasing the number of indexers in a distributed environment
C. Configuring index time field extractions
D. Using lightweight forwarders for data ingestion

Answer: B,C

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 # 20
Which actions enhance the accuracy of Splunk dashboards?(Choosetwo)

A. Disabling drill-down features
B. Avoiding token-based filters
C. Performing regular data validation
D. Using accelerated data models

Answer: C,D

Explanation:
How to Improve Dashboard Accuracy in Splunk?
#1. Using Accelerated Data Models (Answer A)#Increases search speedand ensuresdashboards load faster.
#Provides pre-processed structured dataforreal-time analysis.#Example:ASOC dashboard tracking failed loginsuses an accelerated authentication data model forfaster rendering.
#2. Performing Regular Data Validation (Answer C)#Ensures that the indexed data is accurate and complete.
#Prevents misleading dashboardscaused by incomplete logs or incorrect field extractions.#Example:If afirewall log source stops sending data, regular validation detects missing logsbefore analysts rely on incorrect dashboards.
Why Not the Other Options?
#B. Avoiding token-based filters- Tokensimprovedashboard flexibility; avoiding themreduces usability.#D.
Disabling drill-down features- Drill-downsenhance insightsby allowing analysts to investigate details easily.
References & Learning Resources
#Splunk Dashboard Performance Optimization: https://docs.splunk.com/Documentation/Splunk/latest/Viz
/Dashboards#Using Data Models for Fast and Accurate Dashboards: https://splunkbase.splunk.com#Regular Data Validation for SOC Dashboards: https://www.splunk.com/en_us/blog/security

NEW QUESTION # 21
What are the key components of Splunk's indexing process?(Choosethree)

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

Answer: A,B,C

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 # 22
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