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ISACA AAIR Exam Syllabus Topics:

ISACA Advanced in AI Risk Sample Questions:

1. Which of the following is the MOST important benefit of deploying continuous monitoring and automated anomaly detection for AI models in production?

A) Automated risk register and vulnerability database updates
B) Timely identification of adversarial intrusions and unauthorized parameter changes
C) Improved alignment of model outputs and behavior with organizational strategic goals
D) Enhanced interpretability and transparency of model decisions

2. A risk practitioner is performing a post-implementation review for an AI system used for credit scoring.
Which of the following is MOST important for the risk practitioner to confirm?

A) The AI system's decisions are explainable and fair.
B) Access token runtime is logged and timestamped.
C) Performance metrics are frequently communicated to stakeholders.
D) Employees find the AI system easy to learn and use.

3. A risk practitioner is assessing risk in a newly implemented AI system integrated into an organization's business processes. Which of the following is the MOST important consideration for the risk practitioner?

A) Criticality and impact of decision-making driven by the AI system
B) AI expertise within the organization's risk management function
C) Level of existing business process automation prior to AI adoption
D) Escalation and approval protocols for AI mitigation measures

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

5. An organization plans to deploy an AI system that ingests multiple sources with varying completeness and accuracy. Which of the following is the risk practitioner's BEST recommendation?

A) Perform post-implementation assessments to identify data integrity issues.
B) Implement continuous real-time quality assurance (QA) processes.
C) Fine-tune model parameters to accommodate variable inputs.
D) Use synthetic data augmentation to supplement training and testing.

Solutions: