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NEW QUESTION # 225
An IS auditor notes that an AI model achieved significantly better results on training data than on test data.
Which of the following problems with the model has the IS auditor identified?
Answer: C
Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIA™ Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs." Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Overfitting, Underfitting, and Generalization"
NEW QUESTION # 226
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?
Answer: A
Explanation:
A "Model Card" is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear "Understanding of model intent and performance context." It allows the organization to determine if a vendor's model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition.
NEW QUESTION # 227
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?
Answer: D
Explanation:
WhenAI outputs are not being reviewed, the primary concern is thatincorrect, biased, or non-compliant decisionsmay go undetected. The best way to address this risk is to implement avalidation process for AI decisions(B), which may include human-in-the-loop checks, sampling-based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance ofsupervision and validation of AI outputs, particularly where decisions have financial, legal, or safety implications.
A larger training dataset (A) does not guarantee correctness or fairness and does not replace oversight.
Regular retraining (C) can be beneficial but can also propagate errors if not validated. Prompt templates (D) are relevant mainly for generative and prompt-based systems, and they do not constitute systematic review of outputs. Therefore, astructured validation processis the most direct and effective control.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions).
ISACA audit guidance on AI decision validation and human oversight.
NEW QUESTION # 228
Which of the following presents the MOST significant barrier to generative AI model explainability?
Answer: C
Explanation:
The rapid evolution of modern generative AI architectures (option B) is the largest barrier to explainability.
Complex deep learning models like LLMs, diffusion models, and transformer-based architectures involve millions or billions of parameters, making it extremely challenging to determine precisely how outputs are produced.
AAIA notes that explainability challenges arise because:
Model structures are highly complex
Parameter interactions are nonlinear
Internal representations are not human-interpretable
Continuous updates make documentation outdated
Training data and latent representations create opaque reasoning chains
NEW QUESTION # 229
Which of the following would be MOST useful for an IS auditor when testing high-impact rare scenarios that have not yet occurred in a production environment?
Answer: C
Explanation:
For " edge cases " or " rare scenarios " (often called " black swan " events), historical data is insufficient because the events haven ' t happened yet. " Synthetic data " is artificially generated data that maintains the statistical properties of real data but includes simulated rare events. This allows auditors to " stress test " how the AI model would react to extreme conditions, such as a massive market crash or a rare medical emergency.
Anonymized or de-identified data only reflects past occurrences. Using synthetic data is a proactive testing strategy to ensure the AI ' s safety and robustness in unpredictable environments.
NEW QUESTION # 230
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