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NEW QUESTION # 58
After deployment, an AI model's output begins to drift outside of the expected range. Which of the following is the development team's BEST course of action?
Answer: A
Explanation:
AAISM emphasizes that when model drift occurs, the best response is not a quick fix but rather to revisit an earlier phase of the AI life cycle to address data quality, retraining, or evaluation processes. Simply taking the model offline halts functionality without resolution, while adjusting hyperparameters or issuing emergency changes treats the symptom rather than the root cause.
Proper governance requires returning to the design or training phases to re-establish stability, accuracy, and compliance of the model. Thus, the correct approach is to return to an earlier AI lifecycle phase.
NEW QUESTION # 59
A healthcare organization plans to use agentic AI for patient scheduling and claims. Management is concerned that the system may act independently and cause compliance or privacy issues.
Which of the following is the BEST way to address this concern?
Answer: B
Explanation:
Conducting an AI impact assessment allows the organization to evaluate privacy, compliance, operational, and autonomy-related risks before deployment. Assessing acceptable levels of autonomy helps ensure the agentic AI system operates within defined governance, legal, and ethical boundaries appropriate for healthcare environments.
NEW QUESTION # 60
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?
Answer: D
Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments
NEW QUESTION # 61
The MOST important consideration when establishing key risk indicators (KRIs) to monitor AI model drift in an operational machine learning (ML) system is adopting KRIs that:
Answer: B
Explanation:
AI model drift occurs when a model's performance or behavior changes over time due to evolving data patterns or environments. KRIs that identify variations in model accuracy and outputs are the most important because they directly indicate whether the model is deviating from expected performance and reliability.
NEW QUESTION # 62
Which of the following information is MOST important to include in a centralized AI inventory?
Answer: A
Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
NEW QUESTION # 63
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