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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
How can you ensure efficient detection tuning?(Choosethree)

A. Perform regular reviews of false positives.
B. Use detailed asset and identity information.
C. Disable correlation searches for low-priority threats.
D. Automate threshold adjustments.

Answer: A,B,D

Explanation:
Ensuring Efficient Detection Tuning in Splunk Enterprise Security
Detection tuning is essential to minimize false positives and improve security visibility.
#1. Perform Regular Reviews of False Positives (A)
Reviewing false positives helps refine detection logic.
Analysts should analyze past alerts and adjust correlation rules.
Example:
Tuning a failed login correlation search to exclude known legitimate admin accounts.
#2. Use Detailed Asset and Identity Information (B)
Enriches detections with asset and user context.
Helps differentiate high-risk vs. low-risk security events.
Example:
A login from an executive's laptop is higher risk than from a test server.
#3. Automate Threshold Adjustments (D)
Dynamic thresholds adjust based on activity baselines.
Reduces false positives while maintaining security coverage.
Example:
A brute-force detection rule dynamically adjusts its alerting threshold based on normal user behavior.
C: Disable correlation searches for low-priority threats # Instead of disabling, adjust the rule sensitivity or lower alert severity.
#Additional Resources:
Splunk Security Essentials: Detection Tuning Guide
Tuning Correlation Searches in Splunk ES

NEW QUESTION # 27
What is the main purpose of Splunk's Common Information Model (CIM)?

A. To extract fields from raw events
B. To normalize data for correlation and searches
C. To compress data during indexing
D. To create accelerated reports

Answer: B

NEW QUESTION # 28
Which action improves the effectiveness of notable events in Enterprise Security?

A. Limiting the search scope to one index
B. Using only raw log data in searches
C. Disabling scheduled searches
D. Applying suppression rules for false positives

Answer: D

Explanation:
Notable events in Splunk Enterprise Security (ES) are triggered by correlation searches, which generate alerts when suspicious activity is detected. However, if too many false positives occur, analysts waste time investigating non-issues, reducing SOC efficiency.
How to Improve Notable Events Effectiveness:
Apply suppression rules to filter out known false positives and reduce alert fatigue.
Refine correlation searches by adjusting thresholds and tuning event detection logic.
Leverage risk-based alerting (RBA) to prioritize high-risk events.
Use adaptive response actions to enrich events dynamically.
By suppressing false positives, SOC analysts focus on real threats, making notable events more actionable.
Thus, the correct answer is A. Applying suppression rules for false positives.
References:
Managing Notable Events in Splunk ES
Best Practices for Tuning Correlation Searches
Using Suppression in Splunk ES

NEW QUESTION # 29
What is the primary function of a Lean Six Sigma methodology in a security program?

A. Enhancing user activity logs
B. Monitoring the performance of detection searches
C. Optimizing processes for efficiency and effectiveness
D. Automating detection workflows

Answer: C

Explanation:
Lean Six Sigma (LSS) is a process improvement methodology used to enhance operational efficiency by reducing waste, eliminating errors, and improving consistency.
Primary Function of Lean Six Sigma in a Security Program:
Improves security operations efficiency by optimizing alert handling, threat hunting, and incident response workflows.
Reduces unnecessary steps in SOC processes, eliminating redundancies in threat detection and response.
Enhances decision-making by using data-driven analysis to improve security metrics and Key Performance Indicators (KPIs).

NEW QUESTION # 30
What are the main steps of the Splunk data pipeline?(Choosethree)

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

Answer: B,D,E

Explanation:
The Splunk Data Pipeline consists of multiple stages that process incoming data from ingestion to visualization.
Main Steps of the Splunk Data Pipeline:
Input Phase (C)
Splunk collects raw data from logs, applications, network traffic, and endpoints.
Supports various data sources like syslog, APIs, cloud services, and agents (e.g., Universal Forwarders).
Parsing (D)
Splunk breaks incoming data into events and extracts metadata fields.
Removes duplicates, formats timestamps, and applies transformations.
Indexing (A)
Stores parsed events into indexes for efficient searching.
Supports data retention policies, compression, and search optimization.

NEW QUESTION # 31
......

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