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Splunk SPLK-5003 Exam Syllabus Topics:

SectionWeightObjectives
Security Capability Selection, Placement and Configuration15%- Security control architecture
  • 1. Technology selection
  • 2. Capability integration
  • 3. Control placement strategies
Advanced Incident Response and Management10%- Incident response architecture
  • 1. Response workflows
  • 2. Investigation processes
  • 3. Incident management optimization
Advanced Threat Intelligence and Analysis5%- Threat intelligence architecture
  • 1. Threat intelligence integration
  • 2. Threat-informed defense
  • 3. Advanced threat analysis
Security Data Management20%- Data architecture design
  • 1. Data lifecycle management
  • 2. Security data onboarding and normalization
  • 3. Data quality and governance
Advanced Automation and Orchestration10%- SOAR architecture
  • 1. Playbook design
  • 2. Security orchestration
  • 3. Workflow automation
Scaling Cybersecurity Defenses and DevSecOps15%- Security architecture at scale
  • 1. Enterprise security operations design
  • 2. DevSecOps integration
  • 3. Scalable defense strategies
Measuring and Improving Security Program Effectiveness15%- Security metrics and performance
  • 1. Program maturity assessment
  • 2. Risk measurement
  • 3. Continuous improvement processes
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 (Q126-Q131):

NEW QUESTION # 126
While working with the Security Automation team, an architect is reviewing a playbook that automates the handling of compromised credentials. The playbook contains the following stages:
- Examine account to ensure that it is not a service or control
account.
- Access all identity platforms and lock the user account.
- Revoke all current sessions (email, VPN, etc.).
The architect points out the potential for the compromised credentials to be used remotely again.
Which of the following actions need to be added to the playbook to alleviate this?

Answer: B

Explanation:
Revoking the compromised user's MFA tokens helps prevent the attacker from reusing stolen authentication material to regain remote access. This closes a common persistence path after account lockout and session revocation by forcing re-enrollment or reauthentication through trusted recovery processes.


NEW QUESTION # 127
A threat hunter is looking for suspicious login activity across multiple different authentication systems and cloud providers. Today, the threat hunter has to query multiple different data sources with unique logic to gather basic information about authentication events. What method provides a simple way to standardize data formats, and look for common activity across different sources?

Answer: A

Explanation:
Data normalization standardizes fields and event structures across different authentication systems and cloud providers. This allows threat hunters to search for common login activity using consistent field names and logic instead of writing separate queries for each unique data source.


NEW QUESTION # 128
Danielle is a security architect at a multinational retail company. She is evaluating threat intelligence feeds to add to her company's security monitoring program. What is the primary benefit that threat intelligence data can provide?

Answer: A

Explanation:
Threat intelligence data adds external context to detection content by identifying known malicious indicators, adversary tactics, infrastructure, malware, and campaign details. This helps security monitoring distinguish higher-risk activity, prioritize investigations, and create more actionable detections.


NEW QUESTION # 129
Which of the following best describes how data science, machine learning, behavioral analysis, and AI improve threat detection compared to traditional detection approaches?

Answer: A

Explanation:
Data science, machine learning, behavioral analysis, and AI improve detection by identifying anomalies, relationships, and patterns that may not match predefined signatures or static rules.
This helps uncover unusual behavior, emerging threats, and subtle attack activity across large or complex datasets.


NEW QUESTION # 130
Which MLTK command can be combined with tstats in an ES detection to apply a machine learning model to search results?

Answer: D

Explanation:
The fit command is used in the Machine Learning Toolkit to train or apply a machine learning model to search results. In an Enterprise Security detection, it can be combined with tstats output so the model can analyze summarized event data efficiently.


NEW QUESTION # 131
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