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ISACA AAIR Exam Syllabus Topics:

ISACA Advanced in AI Risk Sample Questions:

1. A risk practitioner learns that a credit-scoring AI system is exhibiting bias that cannot be eliminated through further training. Which of the following is the risk practitioner's BEST recommendation?

A) Source vendors for a new credit-scoring AI solution.
B) Take the system out of production to avoid harm and potential legal liability.
C) Apply compensating controls that generate offsetting biases in the opposite direction.
D) Request a risk acceptance from senior management.

2. Which of the following is the PRIMARY benefit of tailoring AI governance to an organization's culture and risk tolerance?

A) Automation of risk assessment processes and clearer AI risk accountability
B) Higher stakeholder acceptance rates and more appropriate AI risk policies
C) Enhanced AI training programs and staff reskilling initiatives
D) Improved AI model explainability and regulatory compliance

3. A manufacturing organization has implemented an autonomous navigation system for warehouse operations.
Which of the following should a risk practitioner regard as the MOST significant concern?

A) The organization uses outside contractors to address the lack of in-house AI knowledge.
B) The system is used to accelerate just-in-time warehouse processes.
C) The system is unable to learn from complex situations not encountered during training.
D) The deep neural network used by the system contains datasets with proprietary information.

4. Which of the following is a risk practitioner's BEST justification for embedding AI risk considerations into acceptable use policies?

A) Applying uniform risk controls across diverse business functions
B) Maintaining alignment of enterprise tolerance across decision-making systems
C) Assigning AI risk accountability to business unit leadership
D) Addressing the potential for shadow AI by defining an allow list for AI tools

5. Which of the following would be of GREATEST concern to a risk practitioner reviewing the testing and validation of an AI-driven technical support system?

A) Infrequent incorporation of updated training datasets
B) Insufficient encryption of data at rest and in transit
C) Excessive dependence on manual sampling
D) Inaccurate outputs resulting from model drift

Solutions: