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| Section | Objectives |
|---|---|
| Topic 1: AI Auditing Tools and Techniques | |
| Topic 2: AI Governance and Risk | |
| Topic 3: AI Operations |
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NEW QUESTION # 71
Which of the following components would MOST effectively address cumulative benefits as part of reinforcement learning (RL)?
Answer: D
Explanation:
In Reinforcement Learning, the goal is to maximize long-term rewards. While the " Reward Function " provides immediate feedback for an action, the " Value Function " estimates the " cumulative benefit " or the total expected reward an agent can gain from a particular state moving forward. For an auditor, understanding the value function is crucial because it governs the agent ' s long-term strategy and decision-making logic. If the value function is poorly defined, the AI might take actions that yield immediate points but lead to long- term failure or unethical shortcuts.
NEW QUESTION # 72
An IS auditor is planning an audit of an AI medical prognosis system. Which of the following is the BEST way to test model transparency?
Answer: C
Explanation:
SHAP (Shapley Additive Explanations) is the leading, industry-recognized method for explaining complex AI model predictions.
AAIA specifically identifies SHAP as a robust technique for transparency in high-impact systems such as medical AI.
SHAP provides:
* Feature contribution breakdowns
* Visual explanation of each decision
* Global and local interpretability
* Quantifiable insights into model logic
* Compliance-friendly justification of predictions
Options A and B relate to integration and stress testing, not transparency.
Option C validates outputs but does not explain how the model reached them.
Thus, SHAP is the best approach for achieving explainability in medical AI.
References:
AAIA Domain 5: Explainability Techniques
AAIA Domain 3: Model Validation and Auditability
NEW QUESTION # 73
Which of the following would pose the GREATEST risk when reviewing AI acceptable use training content?
Answer: D
Explanation:
The non-deterministic nature of AI refers to the fact that modern AI systems--especially generative AI, reinforcement learning, and probabilistic models--do not always produce the same output even when given identical inputs. According to AAIA's ethical and operational guidance, understanding non-determinism is essential for:
Managing expectations of AI behavior
Preventing overreliance on AI outputs
Identifying hallucinations or inconsistent outcomes
Ensuring human oversight remains in place
Supporting proper audit trails and accountability
Upholding professional skepticism toward AI-generated outputs
If training does not explain non-determinism, employees may mistakenly believe AI outputs are always authoritative or correct, which can lead to unsafe reliance, ethical violations, and audit failures.
NEW QUESTION # 74
Which of the following components would MOST effectively address cumulative benefits as part of reinforcement learning (RL)?
Answer: D
Explanation:
In Reinforcement Learning, the goal is to maximize long-term rewards. While the "Reward Function" provides immediate feedback for an action, the "Value Function" estimates the
"cumulative benefit" or the total expected reward an agent can gain from a particular state moving forward. For an auditor, understanding the value function is crucial because it governs the agent's long-term strategy and decision-making logic. If the value function is poorly defined, the AI might take actions that yield immediate points but lead to long-term failure or unethical shortcuts.
NEW QUESTION # 75
Which of the following is the MOST important task when gathering data during the AI system development process?
Answer: C
NEW QUESTION # 76
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