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NEW QUESTION # 151
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
Answer: A
Explanation:
Representativeness means that training data accurately reflects the current real-world environment in which the AI system operates. The BEST way to ensure this is by verifying that the training data remains relevant and aligned with evolving real-world conditions (C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
NEW QUESTION # 152
An AI healthcare diagnostic tool requires large volumes of patient data, raising concerns about privacy and data breaches. Which of the following is the MOST effective strategy to mitigate this risk?
Answer: D
Explanation:
The most effective strategy to protect sensitive patient data is to use synthetic data or anonymized datasets for model training. This reduces exposure of personally identifiable information while allowing the model to learn meaningful medical patterns.
AAIA emphasizes privacy-by-design, de-identification, and minimal use of raw personal data in high-risk sectors such as healthcare. Anonymization and synthetic data significantly reduce the risk of re-identification or breach-related harm.
NEW QUESTION # 153
Which of the following is the MOST important course of action for an organization prior to allowing end users to utilize an AI tool?
Answer: C
Explanation:
An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIA™ Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.
"AI acceptable use policies promote governance by clearly outlining the dos and don'ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards." Other actions (B, C, D) are important in operations and risk management but should follow the establishment of governance protocols through a usage policy. Hence, A is the highest-priority prerequisite.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Policy Frameworks for End-User AI Interaction"
NEW QUESTION # 154
Which of the following is the BEST approach to mitigate the risk of AI model degradation?
Answer: D
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 # 155
An IS auditor relies on generative AI to document audit narratives from walkthrough meetings.
The final version includes generic language and misses key exceptions. What safeguard could improve the reliability of AI-generated documents?
Answer: A
Explanation:
When generative AI produces generic or "vague" summaries, it is often because it lacks the specific context of the meeting. "Prompt engineering and contextual anchoring" involve providing the AI with clear instructions (e.g., "focus on control exceptions") and anchoring the request in specific data (e.g., "refer only to the attached meeting transcript"). According to ISACA, this technique directs the model's attention and reduces the likelihood of hallucinations or omissions.
Increasing "temperature" (Option A) would actually make the model more random and less reliable. Anchoring ensures the AI stays grounded in the actual facts of the audit walkthrough.
NEW QUESTION # 156
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