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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Risk | 33% | - AI Risk Management - AI Governance and Program Management - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs |
| Topic 2: AI Operations | 46% | - AI System Lifecycle and Deployment - Incident Management and Resilience - Operational Controls and Readiness - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation |
| Topic 3: AI Auditing Tools and Techniques | 21% | - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Planning and Design - Data Quality and Analytics for AI Audit |
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質問 # 37
An organization's fraud detection model achieves high accuracy on its initial data set but performs poorly in production. After a complex neural network was trained, the training accuracy was significantly higher than the validation accuracy. Which of the following is the MOST likely cause?
正解：D
解説：
"Overfitting" occurs when a complex model, such as a deep neural network, learns the "noise" and specific details of the training data rather than the general underlying patterns. A clear indicator of overfitting is a large gap between training performance and validation/test performance. According to the AAIATM manual, this makes the model brittle and unable to generalize to new, unseen data in a production environment. To mitigate this, auditors should recommend techniques such as regularization, dropout, or simplifying the model architecture.
質問 # 38
An organization ' s fraud detection model achieves high accuracy on its initial data set but performs poorly in production. After a complex neural network was trained, the training accuracy was significantly higher than the validation accuracy. Which of the following is the MOST likely cause?
正解：D
解説：
" Overfitting " occurs when a complex model, such as a deep neural network, learns the " noise " and specific details of the training data rather than the general underlying patterns. A clear indicator of overfitting is a large gap between training performance and validation/test performance. According to the AAIA™ manual, this makes the model brittle and unable to generalize to new, unseen data in a production environment. To mitigate this, auditors should recommend techniques such as regularization, dropout, or simplifying the model architecture. Underfitting (Option C) would result in poor performance across both datasets.
質問 # 39
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?
正解：D
解説：
While regulatory compliance is essential, AAIA underlines that ethical integrity must guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is to ensure ethical integrity throughout the AI life cycle (C) -- from data collection and model design to deployment, monitoring, and retirement.
質問 # 40
Which of the following is the BEST way to support the development and design of high-risk AI systems?
正解：D
解説：
The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high- risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase.
Therefore, option C is the most effective practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Governance and Management"
質問 # 41
An organization wants to reduce the risk of over-reliance ("automation bias") among employees using an AI decision-support tool. Which control is MOST effective?
正解：C
解説：
Automation bias -- the tendency to over-trust automated outputs -- is best mitigated through training that encourages critical evaluation, combined with transparency features (confidence scores, rationale) that support informed human judgment.
質問 # 42
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