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Splunk SPLK-5002 Actual Exam Dumps & Composite Test SPLK-5002 Price

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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

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 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.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q65-Q70):

NEW QUESTION # 65
How can you incorporate additional context into notable events generated by correlation searches?

A. By adding enriched fields during search execution
B. By using the dedup command in SPL
C. By configuring additional indexers
D. By optimizing the search head memory

Answer: A

Explanation:
In Splunk Enterprise Security (ES), notable events are generated by correlation searches, which are predefined searches designed to detect security incidents by analyzing logs and alerts from multiple data sources. Adding additional context to these notable events enhances their value for analysts and improves the efficiency of incident response.
To incorporate additional context, you can:
Use lookup tables to enrich data with information such as asset details, threat intelligence, and user identity.
Leverage KV Store or external enrichment sources like CMDB (Configuration Management Database) and identity management solutions.
Apply Splunk macros orevalcommands to transform and enhance event data dynamically.
Use Adaptive Response Actions in Splunk ES to pull additional information into a notable event.
The correct answer is A. By adding enriched fields during search execution, because enrichment occurs dynamically during search execution, ensuring that additional fields (such as geolocation, asset owner, and risk score) are included in the notable event.
References:
Splunk ES Documentation on Notable Event Enrichment
Correlation Search Best Practices
Using Lookups for Data Enrichment

NEW QUESTION # 66
What is the role of aggregation policies in correlation searches?

A. To group related notable events for analysis
B. To normalize event fields for dashboards
C. To automate responses to critical events
D. To index events from multiple sources

Answer: A

Explanation:
Aggregation policies in Splunk Enterprise Security (ES) are used to group related notable events, reducing alert fatigue and improving incident analysis.
Role of Aggregation Policies in Correlation Searches:
Group Related Notable Events (A)
Helps SOC analysts see a single consolidated event instead of multiple isolated alerts.
Uses common attributes like user, asset, or attack type to aggregate events.
Improves Incident Response Efficiency
Reduces the number of duplicate alerts, helping analysts focus on high-priority threats.

NEW QUESTION # 67
What methods improve risk and detection prioritization?(Choosethree)

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

Answer: A,B,C

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 # 68
What is the main purpose of Splunk's Common Information Model (CIM)?

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

Answer: C

Explanation:
What is the Splunk Common Information Model (CIM)?
Splunk's Common Information Model (CIM) is a standardized way to normalize and map event data from different sources to a common field format. It helps with:
Consistent searches across diverse log sources
Faster correlation of security events
Better compatibility with prebuilt dashboards, alerts, and reports
Why is Data Normalization Important?
Security teams analyze data from firewalls, IDS/IPS, endpoint logs, authentication logs, and cloud logs.
These sources have different field names (e.g., "src_ip" vs. "source_address").
CIM ensures a standardized format, so correlation searches work seamlessly across different log sources.
How CIM Works in Splunk?
#Maps event fields to a standardized schema#Supports prebuilt Splunk apps like Enterprise Security (ES)
#Helps SOC teams quickly detect security threats
#Example Use Case:
A security analyst wants to detect failed admin logins across multiple authentication systems.
Without CIM, different logs might use:
user_login_failed
auth_failure
login_error
With CIM, all these fields map to the same normalized schema, enabling one unified search query.
Why Not the Other Options?
#A. Extract fields from raw events - CIM does not extract fields; it maps existing fields into a standardized format.#C. Compress data during indexing - CIM is about data normalization, not compression.#D. Create accelerated reports - While CIM supports acceleration, its main function is standardizing log formats.
References & Learning Resources
#Splunk CIM Documentation: https://docs.splunk.com/Documentation/CIM#How Splunk CIM Helps with Security Analytics: https://www.splunk.com/en_us/solutions/common-information-model.html#Splunk Enterprise Security & CIM Integration: https://splunkbase.splunk.com/app/263

NEW QUESTION # 69
Which of the following actions improve data indexing performance in Splunk?(Choosetwo)

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

Answer: A,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 # 70
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