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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain penetration testing concepts |
| Topic 2: Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response |
| Topic 3: Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts |
| Topic 4: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud |
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NEW QUESTION # 78
A malware sample alters itself slightly each time it runs to evade signature detection. What technique is this?
Answer: B
Explanation:
Polymorphic malware changes its code structure while maintaining functionality.
NEW QUESTION # 79
A management team is concerned about an unexpected cost increase for a public-facing AI chatbot.
Which of the following should a security administrator examine first to determine the root cause?
Answer: B
Explanation:
Basic Concept: AI chatbot operational costs are primarily driven by token consumption - the number of tokens processed in requests and generated in responses. Unexpected cost increases in LLM-based chatbots almost always trace back to abnormal token usage patterns. CompTIA SecAI+ Study Guide covers AI cost monitoring and token-based billing under securing AI systems.
Why D is Correct: Model token usage logs directly show how many tokens are being consumed per request, by which users or endpoints, and whether usage has increased abnormally. Examining token usage data is the most direct path to identifying the root cause of unexpected cost increases - whether from a denial-of-wallet attack, user abuse, a new feature generating verbose responses, or legitimate organic growth in usage. This is the first and most relevant examination point for LLM cost analysis.
Why A is Wrong: Firewall logs capture network-level traffic information. While they can reveal unusual access patterns or volumes, they do not contain token consumption data that directly explains LLM billing increases.
Why B is Wrong: WAF rules define filtering policies for web traffic. Reviewing rule configurations does not reveal whether token usage has increased or why costs have risen; it shows security policy settings rather than consumption metrics.
Why C is Wrong: Vector database IOPS performance measures how quickly the database processes read and write operations. While relevant to RAG system performance, IOPS metrics do not directly explain LLM API cost increases driven by token consumption.
NEW QUESTION # 80
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
See Explanation below for complete solution for this PBQ.
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.
NEW QUESTION # 81
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?
Answer: A
NEW QUESTION # 82
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: C
Explanation:
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.
NEW QUESTION # 83
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