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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Risk | 33% | - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - AI Risk Management |
| Topic 2: AI Operations | 46% | - Incident Management and Resilience - Operational Controls and Readiness - AI System Lifecycle and Deployment - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk |
| Topic 3: AI Auditing Tools and Techniques | 21% | - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Planning and Design |
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NEW QUESTION # 107
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: A
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 AAIATM 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."
NEW QUESTION # 108
Which of the following is the MOST important task when gathering data during the AI system development process?
Answer: A
NEW QUESTION # 109
A health organization has deployed an AI model to analyze chest X-rays. The model reports high accuracy, but thresholds are unclear and performance is not broken down by patient demographics. Why is accuracy alone insufficient to evaluate this model?
Answer: A
Explanation:
The ISACA AAIA™ manual warns against " Aggregate Accuracy " in high-risk applications like healthcare.
A model might be 95% accurate overall but have a 40% error rate for a specific minority demographic (e.g., based on age or ethnicity). This creates " Hidden Fairness and Safety Risks " where patients from certain groups are consistently misdiagnosed. Auditors must recommend " Stratified Evaluation, " where performance metrics (Precision, Recall) are calculated separately for each subgroup to ensure equitable outcomes and patient safety.
NEW QUESTION # 110
Which of the following techniques is BEST to use when there is a limited dataset of detailed images available to train a convolutional neural network (CNN) model?
Answer: D
Explanation:
When data is scarce, training a deep CNN from scratch is likely to result in overfitting. "Transfer learning" allows an organization to take a model previously trained on a massive dataset (like ImageNet) and "fine-tune" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits.
NEW QUESTION # 111
Which of the following should be done FIRST when developing an incident management process for AI threats?
Answer: B
Explanation:
The AAIA framework states that incident response begins with roles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
Ownership of monitoring
Chain of command for incident decisions
Clear responsibility for documentation
Communication pathways
Allocation of resources for containment
NEW QUESTION # 112
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