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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI risk monitoring and continuous improvement - AI governance communication and reporting - AI risk assessment and treatment strategies - Enterprise AI risk program design |
| 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 development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification |
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NEW QUESTION # 169
An organization is launching multiple generative AI use cases. The risk practitioner needs to ensure that AI risk decisions align with business objectives and approved risk boundaries of the enterprise.
What is the best action to take?
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. AI risk should be explicitly included in enterprise risk appetite and tolerance so use cases are judged against approved business boundaries. A risk register and data controls are useful, but appetite and tolerance connect treatment decisions directly to business objectives. This makes option D, Include AI in risk appetite and tolerance, 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 # 170
Which of the following is the PRIMARY reason to lower AI model temperature?
Answer: B
Explanation:
Temperature is a hyperparameter in language model generation that controls output randomness. Lower temperatures make the model more deterministic-concentrating probability mass on the most likely tokens and producing more consistent, predictable outputs. Higher temperatures introduce more randomness and diversity.
Why B is Correct: According to ISACA AAIR model configuration guidance, lowering model temperature is primarily used to enhance consistency and accuracy of outputs. In production applications requiring reliable, reproducible responses-such as customer service, compliance reporting, or technical documentation-lower temperature ensures the model consistently generates the most appropriate response based on its learned knowledge, reducing variability and improving output quality.
Why A is Wrong: Temperature adjustment does not directly mitigate bias. Bias in AI models is a function of training data and model architecture, not output randomness. A biased model at low temperature will consistently generate biased outputs; lowering temperature may actually make bias more persistent by reducing variation.
Why C is Wrong: Diversifying ideas and recommendations is achieved by increasing temperature, not lowering it. Higher temperature is used for creative tasks where variety is valuable; lower temperature is used for tasks requiring precision and consistency.
Why D is Wrong: Model temperature has no direct relationship to computational energy consumption. Energy use is primarily driven by model size, computation requirements, and inference frequency-not the temperature parameter.
NEW QUESTION # 171
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
Answer: D
Explanation:
Different AI model architectures are optimized for different tasks. Content creation requires a model that can generate novel outputs-text, images, audio, or code-rather than classify, cluster, or optimize decisions based on rules or rewards.
Why A is Correct: According to ISACA AAIR AI technology selection guidance, generative models are specifically designed to synthesize new content by learning the underlying probability distributions of training data. They can produce novel, contextually appropriate outputs-exactly what content creation requires.
Large language models (LLMs), diffusion models, and GANs are generative architectures designed for this purpose.
Why B is Wrong: Unsupervised clustering groups existing data points by similarity but does not generate new content. It is used for pattern discovery and segmentation, not creative output generation.
Why C is Wrong: Rule-based expert systems execute predefined logic trees and cannot produce novel content beyond the rules explicitly encoded. They are rigid, deterministic systems unsuitable for open-ended content creation.
Why D is Wrong: Reinforcement learning optimizes decision sequences to maximize cumulative rewards. It is suited for sequential decision-making tasks (games, robotics, recommendation systems) but is not the appropriate architecture for direct content generation.
NEW QUESTION # 172
Which of the following BEST helps to ensure an AI system ' s potential human rights harms are managed?
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. Potential human-rights harms are best managed through recurring stakeholder engagement in risk treatment because affected people may reveal impacts that technical metrics or internal policy mapping miss. Ongoing participation supports identification, escalation, and remediation of evolving harms. This makes option C, Engaging stakeholders in recurring risk treatment processes, 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 # 173
A risk practitioner assesses a new AI system and determines that the risk is within the organization's risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?
Answer: C
Explanation:
Even when an AI system is initially assessed as within risk tolerance, its risk profile evolves as the system encounters new data, the operational environment changes, and model performance drifts. Controls that were effective at deployment may become insufficient as these changes accumulate.
Why C is Correct: The ISACA AAIR operational monitoring guidance identifies continuous monitoring for data and performance drift as the most important mechanism for maintaining control effectiveness over time.
Drift detection provides early warning when the AI system begins behaving differently from its validated state-enabling timely control adjustments before risk tolerance is breached. This is particularly critical because AI systems can degrade gradually in ways not visible without active monitoring.
Why A is Wrong: Framework alignment establishes the control baseline but does not actively verify that controls remain effective as the system evolves. Frameworks provide structure; monitoring provides assurance.
Why B is Wrong: Security and risk awareness training is an important human capability development activity but does not detect technical changes in AI system behavior. Training does not substitute for technical monitoring.
Why D is Wrong: Periodic compliance reviews occur at scheduled intervals and may miss drift that develops between review cycles. Continuous monitoring provides real-time detection that periodic reviews cannot match.
NEW QUESTION # 174
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