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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
Topic 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 3
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.

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ISACA Advanced in AI Audit Sample Questions (Q210-Q215):

NEW QUESTION # 210
Which of the following is the GREATEST risk associated with the use of AI coding tools by software developers?

Answer: A

Explanation:
AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
* Data breaches
* Injection attacks
* Authentication bypasses
* Supply chain compromise
* Privilege escalation
Excessive reliance (A) affects productivity but not security.
Human bias (B) is possible but not as severe as security flaws.
Training complexity (D) is not a risk to AI system integrity.
Therefore, the introduction of security vulnerabilities is the greatest risk.
References:
AAIA Domain 2: AI Risks in Software Development
AAIA Domain 5: Security and Ethical Development Practices


NEW QUESTION # 211
The PRIMARY purpose of maintaining an audit trail in AI systems is to:

Answer: C


NEW QUESTION # 212
Which of the following BEST describes the purpose of a "model validation" function that is independent from the model development team?

Answer: A

Explanation:
Independent model validation mirrors the "second line of defense" concept in risk management -- separating those who build the model from those who challenge and approve it reduces conflict of interest and confirmation bias.


NEW QUESTION # 213
What should be done FIRST when an AI-powered chatbot starts giving incorrect financial advice after a backend API change?

Answer: C

Explanation:
When an AI system begins givingincorrect financial advice, the FIRST step is tosuspend the chatbot(D) to prevent ongoing harm. AAIA emphasizes protecting users from unsafe decisions as the top priority in AI operations.
Suspension allows the organization to:
* Stop distribution of harmful advice
* Assess impact and identify faulty API dependencies
* Prevent regulatory or customer harm
* Conduct root-cause analysis safely
Retraining (C) is corrective but must occurafterassessment. Adding rules (B) risks masking deeper issues.
Speed patches (A) are irrelevant to correctness.
References:
AAIA Domain 2: Incident Management, Safety Controls, and Output Validation


NEW QUESTION # 214
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

Answer: C

Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial " perturbations " -small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively " smooths " the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms. Defensive distillation specifically targets the model ' s resilience to technical exploitation.


NEW QUESTION # 215
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