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ISACA AAIR Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI model and data risk identification - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation | |
| Topic 2: AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk assessment and treatment strategies - Enterprise AI risk program design - AI risk monitoring and continuous improvement |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases |
ISACA Advanced in AI Risk Sample Questions:
Question #1
A risk practitioner reviews an AI model that ingests diverse external feeds and determines that their reliability is not consistent. Which of the following BEST mitigates this risk?
A. Updating to the latest model version to accurately reflect real-world data changes
B. Weighting historical records over recent samples to limit induced variance
C. Establishing data provenance and implementing stage gate quality reviews
D. Reducing the diversity of the external feeds and the number of classes
Question #2
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
A. Avoiding noncompliance with regulations for handling sensitive data
B. Addressing statistical distortions in training data that could cause model bias
C. Reducing computational overhead and associated cost
D. Ensuring model training processes can be efficiently executed
Question #3
Which of the following BEST helps to ensure the success of an AI risk management plan?
A. Continuous validation of security controls for AI training datasets
B. Protection mechanisms for proprietary organizational information and trade secrets
C. Standardization of risk assessment criteria across relevant use cases
D. Robust structures for governance and ethical oversight
Question #4
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
A. a generative model capable of synthesizing samples from an underlying distribution.
B. a rule-based expert system driven by explicit decision rules and domain knowledge.
C. a reinforcement learning model that optimizes sequential actions through reward signals.
D. an unsupervised clustering model that groups observations by similarity metrics.
Question #5
A risk practitioner learns that an AI system used by a manufacturer for quality control (QC) has produced inaccurate responses that could potentially impact user safety. Which of the following is the risk practitioner ' s BEST recommendation to mitigate this risk?
A. Augment the model with synthetic data.
B. Conduct bias and fairness testing.
C. Provide AI prompt engineering training.
D. Implement human-in-the-loop reviews.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: B | Question #3 Correct Answer: D | Question #4 Correct Answer: A | Question #5 Correct Answer: D |
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