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| Section | Objectives |
|---|---|
| AI Governance and Risk | |
| AI Auditing Tools and Techniques | |
| AI Operations |
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NEW QUESTION # 45
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card - Electrical Grid Predictive Maintenance Model
Model Information:
Description: AI model designed to predict maintenance needs for
electrical grid components, reduce unplanned downtime, and improve grid reliability.
Inputs: Real-time sensor data, historical maintenance records, and
operational logs.
Outputs: Maintenance needs predictions for 60 & 90 days.
Evaluation:
Approach: Cross-validation and validation of accuracy, precision, and
recall.
Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%
Answer: D
Explanation:
The F1 score is the harmonic mean of precision and recall, offering a balanced measure of model accuracy, especially when there is an imbalance in the classes (e.g., more "no-maintenance" than "needs-maintenance" outcomes). According to the AAIATM Study Guide, the F1 score is used to evaluate how well the model identifies true positives while balancing the risk of false positives and false negatives.
"An F1 score summarizes the model's ability to correctly identify relevant events--in this case, true maintenance needs. A 76% F1 score means the model is relatively balanced and effective at catching maintenance requirements without generating too many false alerts."
NEW QUESTION # 46
Which control BEST addresses the risk of "model extraction" attacks, where an attacker queries an AI system to reverse-engineer its logic?
Answer: B
Explanation:
Model extraction attacks rely on repeated, systematic querying. Rate limiting and anomaly detection on API usage patterns reduce the attacker's ability to reconstruct the model.
NEW QUESTION # 47
Which of the following provides the BEST evidence that an organization's AI integration is aligned with its overall technology strategy?
Answer: A
Explanation:
The BEST evidence of alignment between AI integration and technology strategy is thatclear KPIs(A) have been defined for AI systems and tied to strategic objectives, measurable outcomes, and governance oversight.
KPIs demonstrate that AI initiatives are monitored, performance is evaluated, and outcomes align with enterprise value.
Policies (B) are foundational but do not demonstrate strategic alignment. Innovation culture (C) is broad and subjective. AI expertise (D) indicates capability, not alignment. AAIA stresses linking AI outcomes to business goals throughmetrics, monitoring, and governance dashboards.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Strategy Alignment.
NEW QUESTION # 48
The accuracy of an AI model used for product recommendations, trained on data from a specific year, is declining two years later as customer buying patterns have shifted toward sustainable products. Which of the following is the MOST likely cause?
Answer: A
Explanation:
Data drift (also known as model drift or concept drift) refers to the phenomenon where the statistical properties of the target variable or the input data change over time, leading to a decline in model performance.
In this scenario, the fundamental shift in customer preferences toward sustainability represents a change in the real-world environment that the model ' s original training data no longer reflects. As stated in the AAIA™ guidelines, models operating in dynamic environments like retail require continuous monitoring to detect when historical patterns are no longer predictive. Covariate shift (Option D) is a sub-type of drift specifically involving changes in the distribution of input features, but " Data drift " is the broader, most appropriate term for the observed performance degradation.
NEW QUESTION # 49
Which metric would be MOST useful for evaluating whether a binary classification model favors precision over recall, or vice versa?
Answer: B
Explanation:
Precision-recall curves and the F1 score explicitly capture the tradeoff between false positives and false negatives, which is central to understanding classification model tuning decisions.
NEW QUESTION # 50
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