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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI governance communication and reporting |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability |
| Topic 3: AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks - AI model and data risk identification |
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NEW QUESTION # 21
Which of the following is the GREATEST organizational risk when AI performance alerts are not escalated to decision-makers for review and decisioning?
Answer: C
NEW QUESTION # 22
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?
Answer: C
Explanation:
Data quality directly determines AI model accuracy and reliability. When input sources vary in completeness and accuracy, the AI system is exposed to continuous data quality risks that can produce unreliable outputs.
This requires ongoing, real-time quality management rather than periodic or reactive responses.
Why C is Correct: According to ISACA AAIR data quality guidance, implementing continuous real-time QA processes is the most effective approach for managing variable-quality multi-source inputs. Real-time QA identifies and addresses quality issues as data enters the system-before they contaminate model inputs and outputs. This prevents quality problems from accumulating and ensures the model consistently receives the highest-quality available data.
Why A is Wrong: Synthetic data augmentation is useful for addressing data scarcity but does not resolve accuracy and completeness issues in existing real-world sources. Generating synthetic data alongside poor- quality real data does not improve the real data.
Why B is Wrong: Post-implementation assessments are reactive-they identify problems after they have already affected model behavior and potentially produced harmful outputs. Prevention through real-time QA is superior to post-hoc remediation.
Why D is Wrong: Fine-tuning model parameters can improve robustness to input variation but does not address underlying data quality problems. Models trained to tolerate poor data may produce less reliable outputs than models receiving consistently high-quality data.
NEW QUESTION # 23
Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?
Answer: B
Explanation:
Enterprise risk framework integration elevates AI risk management from a technical discipline to a strategic organizational function, ensuring AI risks are considered alongside all other enterprise risks in strategic planning and decision-making.
Why D is Correct: The ISACA AAIR curriculum identifies enterprise integration as the mechanism that enables organization-level oversight and ensures AI risk management aligns with strategic objectives, risk appetite, and governance structures. This integration allows the board and senior management to make informed decisions about AI investment, deployment, and risk acceptance with full awareness of AI's contribution to the organizational risk profile.
Why A is Wrong: KPI benchmarking is an operational performance management activity. While integration may improve KPI accuracy, this is a secondary operational benefit rather than the primary strategic benefit of ERM integration.
Why B is Wrong: Regulatory compliance is improved by integration but represents a specific compliance benefit rather than the primary organizational value. Compliance is an output of good governance, not the purpose of ERM integration.
Why C is Wrong: Cyber threat identification is a security function that benefits from integration but is not the primary benefit. Many AI risks are non-cyber in nature-fairness, accuracy, transparency-and would not be captured by a cyber-focused framing.
NEW QUESTION # 24
A rideshare organization is considering the use of AI for dynamic surge pricing. Which of the following is the MOST important risk to evaluate?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. Dynamic surge pricing directly affects customers and market behavior, creating legal and competition-law exposure that can be more consequential than integration, compute, or ordinary model-staleness issues. Regulatory scrutiny can lead to penalties and restrictions. This makes option B, Potential regulatory inquiries for anti- competitive AI pricing practices, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 25
An organization adopts a third-party AI service under a shared responsibility model. Which of the following is the MOST important area of focus for the risk practitioner?
Answer: B
Explanation:
The shared responsibility model creates complexity in AI governance because control obligations are distributed between the organization and the vendor. The most critical risk is ambiguity about who owns specific controls and who makes decisions when issues arise.
Why D is Correct: The ISACA AAIR framework identifies documented assignment of control ownership as the cornerstone of shared responsibility governance. Without explicit documentation of which controls the organization owns versus which the vendor owns, and who has decision authority in each scenario, gaps and overlaps emerge that allow risks to go unmanaged. Named ownership ensures accountability persists across the shared boundary.
Why A is Wrong: Staff training on procedures is important but addresses operational readiness rather than the fundamental governance challenge of shared responsibility. Training supports a well-structured model but cannot substitute for defined ownership.
Why B is Wrong: Contractual liability clauses are legal protections that determine financial recourse after incidents. While essential, they do not prevent governance gaps from forming during normal operations.
Why C is Wrong: Data pathway testing is a security assurance activity addressing technical controls. It verifies control function but does not establish who owns those controls or what authority they have in the shared model.
NEW QUESTION # 26
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