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| Section | Weight | Objectives |
|---|---|---|
| AI-assisted Security | 24% | - AI for threat detection and response
|
| Securing AI Systems | 40% | - Secure AI development and operations
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
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NEW QUESTION # 102
Which of the following improves the observability and auditing of an AI system?
Answer: A
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 103
Which of the following is a risk addressed by responsible AI?
Answer: B
Explanation:
Basic Concept: Responsible AI is a governance framework addressing risks that arise from AI systems producing outcomes that are unfair, harmful, or contrary to human values. Different risk types fall under different governance domains - some under responsible AI, others under security or operational management. CompTIA SecAI+ Study Guide covers responsible AI risk categories under Domain 4.
Why C is Correct: Response bias occurs when an AI system ' s outputs are systematically skewed against certain groups, topics, or perspectives, reflecting biases embedded in training data or model design. This is a core risk addressed by responsible AI principles including fairness, non-discrimination, and explainability.
Responsible AI frameworks mandate bias detection, assessment, and mitigation to ensure AI responses treat all users and groups equitably.
Why A is Wrong: Model drift describes the degradation of model performance over time as the distribution of real-world data diverges from the training data distribution. While an important operational concern, model drift is primarily a technical performance risk managed through MLOps and monitoring practices, not a core responsible AI governance concern.
Why B is Wrong: Reputational loss is a business risk consequence that may result from various AI failures including biased outputs or privacy violations. It is an outcome or impact rather than a specific risk category that responsible AI frameworks directly address.
Why D is Wrong: Data poisoning is a security attack where adversaries corrupt AI training data to manipulate model behavior. This is a cybersecurity threat managed through security controls and data integrity protections rather than responsible AI ethical governance frameworks focused on fairness and accountability.
NEW QUESTION # 104
A security analyst is preparing a presentation for the sales team that describes the most common vulnerabilities that are specific to AI applications.
Which of the following is the best source for the analyst to consult?
Answer: B
Explanation:
Basic Concept: Identifying AI-specific application vulnerabilities requires consulting a resource that has cataloged and documented the unique vulnerability types that affect AI systems, particularly LLMs. Different security standards serve different purposes, and selecting the right reference for AI application vulnerabilities is essential. CompTIA SecAI+ Study Guide references OWASP for AI application vulnerability guidance.
Why C is Correct: OWASP maintains the OWASP Top 10 for Large Language Model Applications, which specifically catalogs the most critical and common vulnerabilities in AI applications including prompt injection, sensitive information disclosure, excessive agency, insecure output handling, and training data poisoning. This AI-specific vulnerability list is the most directly relevant and accessible resource for a presentation on AI application vulnerabilities.
Why A is Wrong: ISO 27001 is a general information security management system standard covering broad organizational security controls. It does not specifically catalog AI application vulnerabilities or LLM-specific weakness categories.
Why B is Wrong: CWE catalogues software weakness types at a code and design level for traditional software. While some weaknesses apply to AI systems, CWE does not have a dedicated AI application vulnerability taxonomy comparable to the OWASP LLM Top 10.
Why D is Wrong: NIST RMF is a risk management framework providing guidance for managing and reducing information security risk. It is a process framework, not a vulnerability catalog, and does not list specific AI application vulnerability types suitable for a vulnerabilities presentation.
NEW QUESTION # 105
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: A
Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.
NEW QUESTION # 106
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:
A DoS attack through the context window relies on overwhelming the model with excessive or obfuscated input. Prompt filtering prevents such malicious or oversized inputs from being processed, ensuring that the model only receives safe, properly structured data within acceptable limits.
NEW QUESTION # 107
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