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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 (Q318-Q323):

NEW QUESTION # 318
Which of the following is the BEST approach to mitigate the risk of AI model degradation?

Answer: C

Explanation:
" Model degradation " (or model decay) happens when a model ' s performance slowly worsens because the real-world environment has changed since its last training. The most effective safeguard is " Periodic human reviews. " Humans can identify " contextual shifts " or " common-sense errors " that automated monitoring might miss. This " Human-in-the-Loop " (HITL) control ensures that the AI's decisions remain grounded, justifiable, and accurate over time. Relying on model-generated data (Option A) can actually accelerate degradation through a phenomenon known as " Model Collapse, " where the AI begins to learn from its own mistakes.


NEW QUESTION # 319
An IS auditor is performing an inventory audit for a manufacturing organization. Which of the following would BEST enable the auditor to identify types of products without assistance from organizational staff?

Answer: A

Explanation:
Computer vision uses machine learning techniques to identify and classify visual data such as images or videos. In inventory audits, it can be used to recognize product types, scan barcodes, or evaluate storage conditions without human assistance.
"Computer vision is particularly effective in automated environments like manufacturing, where visual data from cameras or sensors can be processed to verify product identification and placement."


NEW QUESTION # 320
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?

Answer: A

Explanation:
Class imbalanceoccurs when one or more classes are underrepresented in the training data. The GREATEST concern ismodel bias(C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate intosystematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).
Data drift (A) refers to changes in data distributions over time-related but distinct. Data quality (B) is broader and may or may not be affected by imbalance. Overfitting (D) is a risk, but class imbalance more directly raises fairness and representativeness concerns rather than overfitting alone. Thus,bias arising from class imbalanceis the auditor's primary concern.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data balancing, bias risk).
ISACA AI ethics and model risk guidance discussing class imbalance and fairness.


NEW QUESTION # 321
During an audit of an AI incident response plan, what is the MOST important element specific to AI (beyond a standard IT incident plan)?

Answer: B

Explanation:
Generic IT incident plans do not typically address AI-specific failure modes; a mature plan must explicitly define detection and containment procedures for issues like drift, poisoning, or biased outputs.


NEW QUESTION # 322
Which of the following techniques would be MOST effective as part of incident management procedures for a prompt injection attack?

Answer: B

Explanation:
Prompt injection attacksinvolve maliciously crafted inputs intended to override system instructions, exfiltrate data, or cause harmful behavior. The most effective control aligned with incident management is to deploy robust input validation and sanitization(C), which includes rules and filters designed to detect and neutralize potentially malicious content before it reaches the model. AAIA's coverage of AI threats and vulnerabilities highlights the importance ofinput validation and secure prompt handlingfor generative AI systems.
Fine-tuning the model (A) is a long-term adaptation, not an immediate incident control. Scanning for code- like structure (B) or excessive special characters (D) may catch some attacks but are too narrow; many prompt injections use natural language. Comprehensiveinput validationand sanitization is the most effective and generalizable incident management response.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI; AI-specific threats and incident management.
ISACA AI security guidance covering prompt injection and input validation controls.


NEW QUESTION # 323
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