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ISACA AAIR Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | - AI model and data risk identification - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation | |
| AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - AI governance communication and reporting |
| 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:
1. An organization deploys an autonomous system that makes decisions affecting compliance with regulations.
If those decisions could potentially produce regulatory breaches, which of the following BEST helps to manage associated liability exposures?
A) Restricting AI deployment to use cases with lower impact and delaying broader operational integration
B) Routing escalations through a single point of contact and prohibiting disclosure of proprietary information
C) Creating a separate compliance program for AI obligations and maintaining distinct reporting channels
D) Retaining documentation that provides explainability for decisions and embedding controls in oversight processes
2. An organization has deployed an AI-powered customer service chatbot. Which of the following BEST helps to ensure the chatbot maintains high accuracy in interpreting and answering customer inquiries?
A) Introducing explainable AI techniques and conducting periodic code reviews
B) Calculating precision and recall scores and increasing model temperature
C) Adopting vendor-recommended thresholds and conducting benchmarking
D) Measuring intent-classification error rates and refining training datasets
3. An organization is integrating AI systems into core business operations and has decided to establish a formal process to align AI initiatives with corporate values. Which of the following is the GREATEST benefit of this decision?
A) The transparency and explainability of AI model decisions is enhanced for all stakeholder groups.
B) Return on investment (ROI) for new AI services can be evaluated more accurately.
C) Ethical principles can be added to AI development and usage after deployment.
D) Executive support for technical training and upskilling related to AI can be more effectively obtained.
4. Which of the following AI capabilities would BEST enable a forecasting system to accurately predict the point at which specific equipment components are likely to fail?
A) Real-time analysis of sensor monitoring data
B) Post-defect identification of complex root causes
C) Recommendation of replacement products
D) Dynamic inventories of spare equipment parts
5. A financial organization is developing an AI model for credit risk assessment. Which of the following is MOST important to ensure the training data supports accurate and unbiased outcomes?
A) Data normalization
B) Dataset diversity
C) Synthetic data augmentation
D) Supervised learning
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |






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