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Splunk SPLK-5003 Exam Syllabus Topics:

Section	Weight	Objectives
Advanced Automation and Orchestration	10%	- Integration with enterprise systems and tools - Automation strategy and governance - Designing scalable SOAR architectures
Scaling Cybersecurity Defenses and DevSecOps	15%	- Security in software development lifecycle - Distributed and high-availability security deployments - Cloud and hybrid environment security design
Security Data Management	20%	- Schema design and Common Information Model (CIM) implementation - Data retention, storage, and archiving strategies - Enterprise-scale data ingestion and normalization - Data quality, validation, and governance
Advanced Threat Intelligence and Analysis	5%	- Threat intelligence lifecycle management - Advanced threat hunting methodologies - Integrating threat data into security architecture
Advanced Incident Response and Management	10%	- Designing incident response frameworks - Orchestrated response workflows - Post-incident activities and continuous improvement
Security Capability Selection, Placement, and Configuration	15%	- Optimization and tuning of security components - Architectural placement and integration design - Evaluating and selecting security technologies
Governance, Risk and Compliance	10%	- Risk assessment and management frameworks - Policy development and enforcement - Aligning security with regulatory requirements
Measuring and Improving Security Program Effectiveness	15%	- Continuous monitoring and improvement processes - Maturity models and capability assessments - Security metrics and KPIs design

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Splunk Certified Cybersecurity Defense Architect Sample Questions (Q63-Q68):

NEW QUESTION # 63
An architect needs to design detections that leverage user behavior baselines to flag anomalies such as impossible travel. Which Splunk technology is best suited?

A. Metrics indexes
B. KV Store lookups
C. Data model acceleration
D. UEBA (User and Entity Behavior Analytics)

Answer: D

Explanation:
Splunk UEBA is purpose-built for behavioral baselining and anomaly detection across users and entities, including detecting anomalies like impossible travel based on statistical and machine learning models.

NEW QUESTION # 64
Which CIM data model would be most relevant for building detections around lateral movement using authentication logs?

A. Endpoint
B. Network Traffic
C. Authentication
D. Change Analysis

Answer: C

Explanation:
The Authentication data model normalizes login/logoff events across sources, making it the appropriate model for detecting patterns like unusual account logons associated with lateral movement.

NEW QUESTION # 65
What is a Software Bill of Materials (SBOM)?

A. An invoice for commercial software license renewal
B. A contract for purchasing software licenses annually
C. A list of third-party components contained within a piece of software
D. A list of dependencies a user must download in addition to a piece of software

Answer: C

Explanation:
A Software Bill of Materials is an inventory of third-party components, libraries, packages, and dependencies included in a software product. It helps organizations understand software supply chain risk, identify vulnerable components, and support compliance and vulnerability management.

NEW QUESTION # 66
Sophia manages data ingestion for her organization's SIEM. The data science team wants to perform real-time analytics on security data and asks Sophia for a copy of all new endpoint telemetry from the current point forward. The SIEM currently collects 15TB of endpoint telemetry every day. Which of the following solutions can Sophia use to best help the data science team?

A. Configure the SIEM to export a CSV report of all new telemetry data every night.
B. Export the last 12 months of telemetry data from the SIEM in OCSF.
C. Use a message bus to send data to both the SIEM and data science team.
D. Export the last 12 months of telemetry data from the SIEM in JSON format.

Answer: C

Explanation:
A message bus is the best solution because it enables endpoint telemetry to be streamed from the point of collection to multiple consumers in real time. This supports both SIEM ingestion and the data science team's analytics needs without relying on large historical exports, delayed batch reports, or inefficient nightly file generation for 15TB of daily telemetry.

NEW QUESTION # 67
How can an organization best improve its Mean Time to Respond (MTTR) metrics?

A. Use a message bus to stream data to a data lake for trend analysis.
B. Implement SOAR playbooks to automatically isolate infected endpoints.
C. Automatically page incident responders when low severity alerts occur.
D. Automatically empty spam folders from end users' mailboxes every 30 days.

Answer: B

Explanation:
SOAR playbooks improve Mean Time to Respond by automating repeatable containment actions, such as isolating infected endpoints. This reduces manual response delays and helps responders act faster when confirmed threats require immediate containment.

NEW QUESTION # 68
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