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Splunk SPLK-5003 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Advanced Incident Response and Management10%- Incident response architecture
  • 1. Incident management optimization
  • 2. Investigation processes
  • 3. Response workflows
Topic 2: Advanced Automation and Orchestration10%- SOAR architecture
  • 1. Workflow automation
  • 2. Security orchestration
  • 3. Playbook design
Topic 3: Scaling Cybersecurity Defenses and DevSecOps15%- Security architecture at scale
  • 1. Enterprise security operations design
  • 2. DevSecOps integration
  • 3. Scalable defense strategies
Topic 4: Measuring and Improving Security Program Effectiveness15%- Security metrics and performance
  • 1. Continuous improvement processes
  • 2. Program maturity assessment
  • 3. Risk measurement
Topic 5: Security Data Management20%- Data architecture design
  • 1. Data lifecycle management
  • 2. Security data onboarding and normalization
  • 3. Data quality and governance
Topic 6: Security Capability Selection, Placement and Configuration15%- Security control architecture
  • 1. Technology selection
  • 2. Control placement strategies
  • 3. Capability integration
Topic 7: Advanced Threat Intelligence and Analysis5%- Threat intelligence architecture
  • 1. Advanced threat analysis
  • 2. Threat-informed defense
  • 3. Threat intelligence integration
Topic 8: Governance, Risk and Compliance10%- Security governance
  • 1. Risk management frameworks
  • 2. Policy alignment
  • 3. Compliance requirements

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Splunk Certified Cybersecurity Defense Architect Sample Questions (Q139-Q144):

NEW QUESTION # 139
Which of the following are valid use cases for the MLTK (Machine Learning Toolkit) in a security context? (Choose all that apply.)

Answer: A,B,C

Explanation:
MLTK supports anomaly detection, predictive analytics (e.g., forecasting), and clustering algorithms applicable to security data; it does not autonomously write SPL queries, which remains a manual/analyst task.


NEW QUESTION # 140
An architect should consider which of the following when evaluating a new cybersecurity SaaS offering for potential introduction into the corporate environment? (Choose all that apply.)

Answer: C,D

Explanation:
When evaluating a cybersecurity SaaS offering, the architect should consider where data will be stored and processed to address regulatory, privacy, and sovereignty requirements. Third-party attestations and certifications help validate the provider's security, compliance posture, and operational controls before introducing the service into the corporate environment.


NEW QUESTION # 141
A global enterprise is experiencing severe performance issues on their Splunk Enterprise Security Search Head due to an overwhelming number of distinct Asset and Identity lookups being performed simultaneously. What is the BEST architectural recommendation to resolve this performance issue?

Answer: D

Explanation:
For very large environments, massive CSV lookups can heavily degrade Search Head performance due to memory and I/O constraints. Consolidating the lists and migrating the Asset and Identity collections to the KV Store greatly improves lookup performance, search efficiency, and resource utilization in Splunk ES.


NEW QUESTION # 142
Which of the following is the primary benefit of using summary indexing for high-volume, long- running statistical searches?

Answer: B

Explanation:
Summary indexing periodically stores the results of expensive searches so that future queries over long time ranges can use the smaller summarized dataset instead of recomputing against full raw data, improving performance.


NEW QUESTION # 143
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?

Answer: D

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 # 144
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