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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operations | 46% | - Third-Party and Supply Chain Risk - AI System Lifecycle and Deployment - Operational Controls and Readiness - Performance Monitoring and Evaluation - Incident Management and Resilience |
| Topic 2: AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - AI Governance and Program Management - AI Risk Management - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs |
| Topic 3: AI Auditing Tools and Techniques | 21% | - Data Quality and Analytics for AI Audit - Audit Planning and Design - Audit Testing and Sampling Methodologies - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques |
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NEW QUESTION # 289
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?
Answer: A
Explanation:
A "Model Card" is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear "Understanding of model intent and performance context." It allows the organization to determine if a vendor's model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition.
NEW QUESTION # 290
Which of the following BEST detects model drift or unexpected changes in AI model outputs?
Answer: D
NEW QUESTION # 291
An IS auditor notes that an AI modelachieved significantly better results on training data than on test data.
After the development, a quality assurance (QA) team checks that all input variables and parameters match the technical design. Which of the following BEST describes this activity?
Answer: D
Explanation:
The ISACA AAIA™ framework distinguishes between " Verification " and " Validation " . " Verification " is a QA process that checks if the system was " built right " -i.e., whether the code, parameters, and architecture match the design documentation. " Validation " (Option A) checks if the " right system " was built-i.e., whether the model actually performs correctly on unseen data and meets business goals. While testing (Option C) identifies the performance gap, the specific act of auditing the model ' s configuration against its technical blueprints is a verification activity.
NEW QUESTION # 292
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: D
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 AAIA™ 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." Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Overfitting, Underfitting, and Generalization"
NEW QUESTION # 293
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?
Answer: D
Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, "algorithmic transparency--enabling auditors and stakeholders to see and understand the rationale behind automated decisions--is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
NEW QUESTION # 294
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