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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - AI Audit Outputs and Reporting - Data Quality and Analytics for AI Audit - Audit Planning and Design - Audit Evidence Collection Techniques - Audit Testing and Sampling Methodologies |
| Topic 2: AI Operations | 46% | - AI System Lifecycle and Deployment - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk - Operational Controls and Readiness - Incident Management and Resilience |
| Topic 3: AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - AI Risk Management - AI Governance and Program Management |
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NEW QUESTION # 241
Which of the following metrics are the BEST indication of a mature and effective approach to an organization's data governance program for its AI systems?
Answer: C
NEW QUESTION # 242
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?
Answer: B
Explanation:
When a dataset lacks sufficient examples of a "minority class" (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to "Augment the data through synthesizing" (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain.
NEW QUESTION # 243
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: B
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIA™ Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Types of AI Learning Models"
NEW QUESTION # 244
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?
Answer: B
Explanation:
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing.
According to the AAIATM Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made--false positives and false negatives--thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed--not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
NEW QUESTION # 245
What is the PRIMARY purpose of establishing AI-specific key risk indicators (KRIs), such as drift thresholds or fairness metric thresholds?
Answer: D
Explanation:
KRIs translate abstract risks (like drift or bias) into measurable thresholds that trigger proactive action, enabling risk management before issues escalate into incidents.
NEW QUESTION # 246
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