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ISACA Advanced in AI Risk Sample Questions:

1. An organization uses multiple external data sources to train its AI models. Which of the following is the risk practitioner's BEST recommendation to protect the organization from data poisoning attacks?

A) Continuous monitoring and anomaly detection for data ingestion pipelines
B) Enhanced regularization and training techniques to limit the influence of anomalies
C) Stringent controls over model code and deployment artifacts
D) Data integrity reviews in response to indications that significant model drift has occurred

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

3. Which of the following BEST mitigates risk associated with evasion attacks on AI models?

A) Anomaly detection
B) Feature importance weighting
C) Predictive analytics
D) API rate limiting

4. An organization depends on multiple external suppliers for AI models and training datasets. Which of the following is MOST important to have in place in order to reduce supply chain risk?

A) Appointment of a vendor risk manager with AI expertise to serve as a single point of contact
B) Verifiable end-to-end provenance and audit trails for externally sourced artifacts
C) Requirement for vendors to provide documentation of model training methods used
D) Standard indemnity clauses in vendor contracts to assign liability responsibilities

5. Which of the following is the PRIMARY benefit of aligning AI risk management with existing organizational governance frameworks?

A) It standardizes AI acquisition processes across organizational business units.
B) It emphasizes the development of specialized functional roles and clarifies AI risk responsibility boundaries.
C) It expedites approval processes for compliance with AI laws and regulations.
D) It promotes consistent enterprise-level oversight of AI activities and aligns decisioning with strategic objectives.

Solutions: