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ISACA Advanced in AI Risk Sample Questions:

1. 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

2. Which of the following AI system considerations BEST mitigates risk associated with model drift?

A) Conducting regular retraining with new relevant datasets
B) Implementing strong access controls based on roles and responsibilities
C) Maintaining existing levels of variance within datasets during preprocessing
D) Restricting the use of automated data validation to low-risk models

3. A risk practitioner assesses a new AI system and determines that the risk is within the organization's risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?

A) Ongoing AI security and risk awareness training
B) Continuous monitoring for data and performance drift
C) Alignment with recognized AI control frameworks
D) Periodic regulatory compliance reviews

4. An organization has deployed generative AI tools broadly but lacks a consistent method to refresh governance policies and controls. Which of the following is the risk practitioner's BEST recommendation?

A) Implement systematic updates and emphasize alignment with emerging regulatory expectations.
B) Establish an ongoing review cadence and codify procedures for reassessment.
C) Schedule annual compliance reviews and integrate audit findings into revision planning.
D) Centralize decision making and concentrate authority within executive leadership and technical owners.

5. Which of the following is the BEST course of action to mitigate risk during model selection of supervised or unsupervised algorithms?

A) Require the use of supervised learning for model training projects.
B) Align algorithmic capabilities to intended use cases.
C) Emphasize the generalization capability of algorithms.
D) Prioritize cost reductions related to computational requirements.

Solutions: