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| Section | Objectives |
|---|---|
| Topic 1: AI Governance and Risk | |
| Topic 2: AI Auditing Tools and Techniques | |
| Topic 3: AI Operations |
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NEW QUESTION # 69
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: B
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 AAIA™ 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." Thus, D is correct. Option A misrepresents recall as predictive accuracy. Option B misinterprets accuracy.
Option C has no direct basis in the excerpt.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Understanding Evaluation Metrics and Model Cards"
NEW QUESTION # 70
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?
Answer: A
Explanation:
Generative AI excels at synthesizing large datasets and technical documentation into understandable insights.
The AAIA™ Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.
"AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping." While summarizing interviews (D) and creating diagrams (B) are helpful, only C directly informs audit planning with actionable intelligence. A (separation of duties) is a later-stage control assessment.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Use in Planning and Scoping"
NEW QUESTION # 71
Which of the following initially provides assurance that the developer correctly interprets and identifies numerical data for balancing prior to inserting into the model?
Answer: A
Explanation:
A data dictionary (A) is the authoritative source for understanding:
Data types and numeric formats
Valid ranges and interpretations
Field definitions and business meaning
Normalization and scaling expectations
Before balancing or preprocessing data, developers must verify that they understand each feature correctly. The AAIA framework emphasizes that misinterpretation of numeric variables often leads to:
Incorrect normalization
Faulty scaling
Skewed class balancing
Inaccurate model training
NEW QUESTION # 72
Which of the following is MOST important to consider when deciding whether to implement an AI solution?
Answer: D
NEW QUESTION # 73
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?
Answer: A
Explanation:
Unchecked AI outputs pose a significant risk because incorrect or biased results may propagate into decision-making processes. The AAIATM Study Guide stresses that validating AI outputs for consistency, accuracy, and reasonableness is a critical responsibility of both operators and auditors.
"Auditors must verify that appropriate output validation mechanisms are in place. Failure to check outputs increases the likelihood of undetected errors influencing downstream processes or decisions."
NEW QUESTION # 74
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