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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 46% | - Third-Party and Supply Chain Risk - Operational Controls and Readiness - AI System Lifecycle and Deployment - Incident Management and Resilience - Performance Monitoring and Evaluation |
| AI Auditing Tools and Techniques | 21% | - AI Audit Outputs and Reporting - Audit Planning and Design - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques |
| AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - AI Models, Considerations, and Requirements - AI Governance and Program Management - Privacy and Data Governance Programs - AI Risk Management |
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質問 # 57
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?
正解：A
解説：
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.
質問 # 58
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?
正解：A
解説：
TheGREATEST concernis when the AI system'sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.
Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).
質問 # 59
Which metric should an IS auditor review to evaluate issues with data collection that could impact AI model training?
正解：D
解説：
The percentage of missing values (option B) directly reflects data collection issues. Missing or incomplete data can degrade model performance, distort feature distributions, and create biased or inaccurate predictions.
AAIA stresses that auditors must evaluate:
* Completeness
* Validity
* Accuracy
* Consistency
Missing values signal failures in upstream processes, including sensors, user inputs, integrations, or data pipelines.
The other metrics are unrelated to raw data integrity:
* Epochs (A) refer to training cycles.
* Percentage of training data (C) concerns dataset partitioning, not quality.
* True positives (D) relate to model performance, not data collection quality.
References:
AAIA Domain 2: Data Quality, Completeness, and Integrity
AAIA Domain 3: Pre-Training Data Validation
質問 # 60
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training dat a. Which of the following is the auditor's BEST recommendation to address this issue?
正解：A
質問 # 61
Which of the following would be the BEST time to create a data governance plan for a new AI solution?
正解：D
解説：
In AI development, "Privacy by Design" and "Governance by Design" are fundamental principles.
The data governance plan should be created "As part of the initial design" phase. This ensures that the requirements for data quality, lineage, labeling, and privacy are integrated into the architecture from the beginning.
質問 # 62
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