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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Compare and contrast types of social engineering attacks |
| 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 - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation |
| Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement identity and account management controls |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures |
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NEW QUESTION # 53
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity.
Which of the following is the most appropriate monitoring solution?
Answer: C
Explanation:
Basic Concept: Monitoring an AI system ' s internal runtime behavior requires deep observability into what the system is doing at the code and function execution level, not just at the perimeter. CompTIA SecAI+ Study Guide addresses AI system observability and runtime monitoring under securing AI infrastructure.
Why D is Correct: Enabling stack call and debugging level traces at the function level provides the highest granularity of visibility into internal operations. This approach exposes what functions are called, in what order, with what inputs, and what is returned, offering genuine insight into the AI system ' s internal activity at runtime precisely as the engineer requires.
Why A is Wrong: A SIEM aggregates and correlates log and event data from multiple sources. While useful for security alerting, it does not inherently provide visibility into internal function-level operations of an AI model at runtime.
Why B is Wrong: A WAF with header logging monitors and filters HTTP traffic at the application boundary.
It captures external request and response data, not the AI system ' s internal runtime mechanics.
Why C is Wrong: Relying on vendor controls and monitoring prompt inputs is a passive, externally-focused approach. It provides no visibility into intermediate computations or internal operations within the AI model itself.
NEW QUESTION # 54
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji.
Which of the following techniques best mitigates this type of attack?
Answer: D
Explanation:
Basic Concept: Context window DoS attacks flood an LLM ' s context with obfuscated content to exhaust processing resources or manipulate model behavior. Attackers may hide large amounts of text behind Unicode characters like emojis. CompTIA SecAI+ Study Guide identifies prompt filtering as the primary defense against input-based attacks on LLMs.
Why D is Correct: A prompt filter inspects incoming inputs before they reach the LLM, detecting and blocking malicious content including obfuscated text hidden behind Unicode characters or emojis. By analyzing input structure, character counts, hidden content, and encoding anomalies, prompt filters can identify and reject attacks that attempt to abuse the context window, preventing resource exhaustion.
Why A is Wrong: Fraud detection systems are designed to identify fraudulent transactions or activities in structured data contexts. They are not designed to inspect LLM prompt structures for obfuscated content attacks on context windows.
Why B is Wrong: LLM-as-a-judge uses a secondary LLM to evaluate the quality or safety of another model ' s outputs. It operates post-generation and cannot prevent a DoS attack that occurs during input processing before output is generated.
Why C is Wrong: Pattern recognition can identify known attack patterns but requires the attack to match pre- learned patterns. Novel obfuscation techniques using Unicode or emoji hiding may evade pattern-based detection without dedicated prompt filtering logic.
NEW QUESTION # 55
Which of the following is most resistant to AI manipulation?
Answer: B
Explanation:
Reducing the attack surface limits the number of potential entry points that attackers or manipulated AI inputs can exploit. This proactive security measure is more resistant to AI-driven manipulation than reactive controls like payload scanning or antivirus.
NEW QUESTION # 56
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?
Answer: D
Explanation:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.
NEW QUESTION # 57
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values. Which of the following job roles would most likely be responsible for correcting this error?
Answer: C
Explanation:
A data engineer is responsible for preparing, cleaning, and formatting data pipelines. When information is incorrectly formatted or missing values, the data engineer ensures data integrity and quality before it is used by AI models.
NEW QUESTION # 58
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