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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q47-Q52):

NEW QUESTION # 47
What methods improve the efficiency of Splunk's automation capabilities? (Choose three)

A. Optimizing correlation search queries
B. Using modular inputs
C. Implementing low-latency indexing
D. Employing prebuilt SOAR playbooks
E. Leveraging saved search acceleration

Answer: A,B,D

Explanation:
How to Improve Splunk's Automation Efficiency?
Splunk's automation capabilities rely on efficient data ingestion, optimized searches, and automated response workflows. The following methods help improve Splunk's automation:
#1. Using Modular Inputs (Answer A)
Modular inputs allow Splunk to ingest third-party data efficiently (e.g., APIs, cloud services, or security tools).
Benefit: Improves automation by enabling real-time data collection for security workflows.
Example: Using a modular input to ingest threat intelligence feeds and trigger automatic responses.
#2. Optimizing Correlation Search Queries (Answer B)
Well-optimized correlation searches reduce query time and false positives.
Benefit: Faster detections # Triggers automated actions in SOAR with minimal delay.
Example: Usingtstatsinstead of raw searches for efficient event detection.
#3. Employing Prebuilt SOAR Playbooks (Answer E)
SOAR playbooks automate security responses based on predefined workflows.
Benefit: Reduces manual effort in phishing response, malware containment, etc.
Example: Automating phishing email analysis using a SOAR playbook that extracts attachments, checks URLs, and blocks malicious senders.
Why Not the Other Options?
#C. Leveraging saved search acceleration - Helps with dashboard performance, but doesn't directly improve automation.#D. Implementing low-latency indexing - Reduces indexing lag but is not a core automation feature.
References & Learning Resources
#Splunk SOAR Automation Guide: https://docs.splunk.com/Documentation/SOAR#Optimizing Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation/ES#Prebuilt SOAR Playbooks for Security Automation: https://splunkbase.splunk.com

NEW QUESTION # 48
Which features are crucial for validating integrations in Splunk SOAR? (Choose three)

A. Evaluating automated action performance
B. Increasing indexer capacity
C. Monitoring data ingestion rates
D. Testing API connectivity
E. Verifying authentication methods

Answer: A,D,E

Explanation:
Validating Integrations in Splunk SOAR
Splunk SOAR (Security Orchestration, Automation, and Response) integrates with various security tools to automate security workflows. Proper validation of integrations ensures that playbooks, threat intelligence feeds, and incident response actions function as expected.
#Key Features for Validating Integrations
1##Testing API Connectivity (A)
Ensures Splunk SOAR can communicate with external security tools (firewalls, EDR, SIEM, etc.).
Uses API testing tools like Postman or Splunk SOAR's built-in Test Connectivity feature.
2##Verifying Authentication Methods (C)
Confirms that integrations use the correct authentication type (OAuth, API Key, Username/Password, etc.).
Prevents failed automations due to expired or incorrect credentials.
3##Evaluating Automated Action Performance (D)
Monitors how well automated security actions (e.g., blocking IPs, isolating endpoints) perform.
Helps optimize playbook execution time and response accuracy.
#Incorrect Answers & Explanations
B: Monitoring data ingestion rates # Data ingestion is crucial for Splunk Enterprise, but not a core integration validation step for SOAR.
E: Increasing indexer capacity # This is related to Splunk Enterprise data indexing, not Splunk SOAR integration validation.
#Additional Resources:
Splunk SOAR Administration Guide
Splunk SOAR Playbook Validation
Splunk SOAR API Integrations

NEW QUESTION # 49
Which features of Splunk are crucial for tuning correlation searches?(Choosethree)

A. Optimizing search queries
B. Reviewing notable event outcomes
C. Using thresholds and conditions
D. Enabling event sampling
E. Disabling field extractions

Answer: A,B,C

Explanation:
Correlation searches are a key component of Splunk Enterprise Security (ES) that help detect and alert on security threats by analyzing machine data across various sources. Proper tuning of these searches is essential to reduce false positives, improve performance, and enhance the accuracy of security detections in a Security Operations Center (SOC).
Crucial Features for Tuning Correlation Searches
#1. Using Thresholds and Conditions (A)
Thresholds help control the sensitivity of correlation searches by defining when a condition is met.
Setting appropriate conditions ensures that only relevant events trigger notable events or alerts, reducing noise.
Example:
Instead of alerting on any failed login attempt, a threshold of 5 failed logins within 10 minutes can be set to identify actual brute-force attempts.
#2. Reviewing Notable Event Outcomes (B)
Notable events are generated by correlation searches, and reviewing them is critical for fine-tuning.
Analysts in the SOC should frequently review false positives, duplicates, and low-priority alerts to refine rules.
Example:
If a correlation search is generating excessive alerts for normal user activity, analysts can modify it to exclude known safe behaviors.
#3. Optimizing Search Queries (E)
Efficient Splunk Search Processing Language (SPL) queries are crucial to improving search performance.
Best practices include:
Using index-time fields instead of extracting fields at search time.
Avoiding wildcards and unnecessary joins in searches.
Using tstats instead of regular searches to improve efficiency.
Example:
Using:
| tstats count where index=firewall by src_ip
instead of:
index=firewall | stats count by src_ip
can significantly improve performance.
Incorrect Answers & Explanation
#C. Enabling Event Sampling
Event sampling helps analyze a subset of events to improve testing but does not directly impact correlation search tuning in production.
In a SOC environment, tuning needs to be based on actual real-time event volumes, not just sampled data.
#D. Disabling Field Extractions
Field extractions are essential for correlation searches because they help identify and analyze security-related fields (e.g.,user,src_ip,dest_ip).
Disabling them would limit the visibility of important security event attributes, making detections less effective.
Additional Resources for Learning
#Splunk Documentation & Learning Paths:
Splunk ES Correlation Search Documentation
Best Practices for Writing SPL
Splunk Security Essentials - Use Cases
SOC Analysts Guide for Correlation Search Tuning
#Courses & Certifications:
Splunk Enterprise Security Certified Admin
Splunk Core Certified Power User
Splunk SOAR Certified Automation Specialist

NEW QUESTION # 50
What are key benefits of using summary indexing in Splunk? (Choose two)

A. Improves search performance on aggregated data
B. Provides automatic field extraction during indexing
C. Reduces storage space required for raw data
D. Increases data retention period

Answer: A,D

Explanation:
Summary indexing in Splunk improves search efficiency by storing pre-aggregated data, reducing the need to process large datasets repeatedly.
Key Benefits of Summary Indexing:
Improves Search Performance on Aggregated Data (B)
Reduces query execution time by storing pre-calculated results.
Helps SOC teams analyze trends without running resource-intensive searches.
Increases Data Retention Period (D)
Raw logs may have short retention periods, but summary indexes can store key insights for longer.
Useful for historical trend analysis and compliance reporting.

NEW QUESTION # 51
Which sourcetype configurations affect data ingestion?(Choosethree)

A. Timestamp extraction
B. Event breaking rules
C. Data retention policies
D. Line merging rules

Answer: A,B,D

Explanation:
The sourcetype in Splunk defines how incoming machine data is interpreted, structured, and stored. Proper sourcetype configurations ensure accurate event parsing, indexing, and searching.
#1. Event Breaking Rules (A)
Determines how Splunk splits raw logs into individual events.
If misconfigured, a single event may be broken into multiple fragments or multiple log lines may be combined incorrectly.
Controlled using LINE_BREAKER and BREAK_ONLY_BEFORE settings.
#2. Timestamp Extraction (B)
Extracts and assigns timestamps to events during ingestion.
Incorrect timestamp configuration leads to misplaced events in time-based searches.
Uses TIME_PREFIX, MAX_TIMESTAMP_LOOKAHEAD, and TIME_FORMAT settings.
#3. Line Merging Rules (D)
Controls whether multiline events should be combined into a single event.
Useful for logs like stack traces or multi-line syslog messages.
Uses SHOULD_LINEMERGE and LINE_BREAKER settings.
C: Data Retention Policies #
Affects storage and deletion, not data ingestion itself.
#Additional Resources:
Splunk Sourcetype Configuration Guide
Event Breaking and Line Merging

NEW QUESTION # 52
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