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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - AI Governance and Program Management - Privacy and Data Governance Programs - AI Risk Management |
| Topic 2: AI Auditing Tools and Techniques | 21% | - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies - Audit Planning and Design - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting |
| Topic 3: AI Operations | 46% | - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment - Operational Controls and Readiness - Third-Party and Supply Chain Risk - Incident Management and Resilience |
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NEW QUESTION # 305
Which of the following is the BEST use of AI to audit relationships for conflicts of interest or collusion?
Answer: B
Explanation:
"Graph Analytics" (or Network Analysis) is the superior AI technique for auditing "relational risks".
It maps entities (people, vendors, employees) as "nodes" and their interactions as "edges". This allows auditors to visualize and detect "Collusion Rings," where multiple parties are connected in ways that standard tabular analysis would miss.
NEW QUESTION # 306
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: B
NEW QUESTION # 307
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?
Answer: A
Explanation:
The risk described is thatcustomer-facing suggestionsmay be inappropriate, insensitive, or offensive. The BEST mitigation is toperform testing of diverse scenarios(B)-including edge cases, demographic variations, and sensitive contexts-to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlightsscenario-based testingandfairness/impact assessmentsas key practices, especially where recommendations directly influence customer interactions.
Increasing data volume (A) does not ensure fairness or sensitivity. Monitoring servers (C) focuses on technical health, not content appropriateness. Threat analysis (D) is important for security but does not directly address emotional or ethical impacts of model outputs. Therefore, structured,diverse scenario testing is the most targeted and effective approach.
References:
ISACA,AAIA Exam Content Outline- Domain 2 & Domain 5: Testing techniques; ethical and user-impact considerations.
ISACA AI guidance on scenario testing for fairness, appropriateness, and user impact.
NEW QUESTION # 308
Which control BEST mitigates the risk of a machine learning model being manipulated through adversarial input perturbations?
Answer: A
Explanation:
Adversarial robustness testing deliberately crafts inputs designed to fool the model, allowing teams to measure and harden against evasion attacks before deployment.
NEW QUESTION # 309
The internal audit department of a large global organization is evaluating the use of an AI-based voice-to-speech tool to document interviews during audits. The tool uploads all recordings to a cloud service provider for transcription. Which of the following is the GREATEST risk?
Answer: D
Explanation:
Audit interviews often contain highly sensitive, proprietary, or even non-public information.
Uploading these recordings to a cloud provider introduces the "Risk of unauthorized access" by the vendor's employees or through a security breach at the vendor's site. According to ISACA, the loss of "Confidentiality" over audit workpapers is a critical failure of professional standards.
NEW QUESTION # 310
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