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ISACA Advanced in AI Risk Sample Questions:

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

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

2. An organization has identified a moderate AI exposure from potential model inaccuracies that could affect internal reporting. The risk falls within the organization's defined tolerance. Which of the following is the BEST course of action?

A) Take the system offline until the model has been retrained and produces more accurate outputs.
B) Accept the inherent risk and continue to monitor performance against organizational thresholds.
C) Adjust the model temperature to its lowest possible value and conduct comprehensive retesting.
D) Allocate more resources for additional human review cycles to identify accuracy and bias in model outputs.

3. Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?

A) Organization-level oversight and strategic alignment
B) More accurate benchmarking of AI key performance indicators (KPIs)
C) Improved compliance with regulatory requirements
D) Rapid identification of cyber threats and risks

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

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) Supervised learning
B) Dataset diversity
C) Data normalization
D) Synthetic data augmentation

Solutions: