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ISACA Advanced in AI Risk Sample Questions:

1. Which of the following is the GREATEST concern when AI risk management operates separately from enterprise risk management (ERM)?

A) Redundant risk documentation and scoring
B) Inconsistent regulatory reporting
C) Reduced return on investment (ROI) due to increased model training costs
D) Lack of strategic control alignment

2. Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?

A) Reducing the initial impacts of data poisoning and exfiltration attacks
B) Providing more rapid identification of overfitting during model training
C) Automating data cleaning and metadata tagging for large datasets
D) Justifying the use of synthetic data to augment smaller datasets

3. Which of the following is the PRIMARY purpose of maintaining comprehensive model cards and documentation?

A) Justifying model use cases
B) Providing model transparency
C) Preserving audit trails
D) Listing technical specifications

4. An organization has deployed an AI system to automate critical data analysis functions. Which of the following is the MOST appropriate way for the risk practitioner to assess the multiple sources of risk associated with this situation?

A) Prioritize the risk factors most likely to generate substantial harm.
B) Document the exploitable technical limitations of all AI system components.
C) Quantify the financial impact of competitors' realized risk events on AI initiatives.
D) Rate each risk factor independently as a basis for ordering mitigation actions.

5. An organization deploys an AI credit scoring model trained on historical financial data that underrepresents certain demographic groups. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?

A) Define specific inclusivity goals and expand data to a broader range of sources.
B) Notify stakeholders that the model may not always reflect standard loan approval thresholds.
C) Implement reporting for model drift and anomalous model decisions.
D) Use unsupervised learning to identify hidden or complex discriminatory patterns in the dataset.

Solutions: