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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - Enterprise AI risk program design - AI governance communication and reporting - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies |
| AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification | |
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
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NEW QUESTION # 83
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems.
Representative sampling combined with explicit bias mitigation addresses both the root cause and manifestation of fairness risk. Label cleanup and synthetic data can help, but neither guarantees representative coverage or equitable outcomes by itself. This makes option D, Representative sampling strategies combined with bias mitigation controls, 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 # 84
Which of the following is the MOST critical component of a disaster recovery plan (DRP) for data, models, and AI infrastructure?
Answer: A
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Disaster recovery for AI requires tested restoration processes for production-ready models and supporting artifacts, with version control and remote or segregated storage. Policies and insurance do not restore systems, while broader supply-chain issues belong to continuity planning. This makes option C, Restoration processes for production-ready AI models with version control and remote storage, 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 # 85
Which of the following is the MOST appropriate key performance indicator (KPI) for the effectiveness of a targeted AI risk awareness training program?
Answer: D
Explanation:
Training program effectiveness KPIs must measure behavioral change-whether training has actually altered how participants act in their work environment-rather than knowledge acquisition or adoption rates. The most meaningful behavioral signal for AI risk awareness training is whether trained users report suspicious activity.
Why B is Correct: According to ISACA AAIR training effectiveness measurement guidance, the number of AI irregularities and potential tampering incidents reported by users is the most appropriate behavioral KPI for risk awareness training effectiveness. When users report unusual AI behavior, this demonstrates they have internalized training concepts well enough to recognize anomalies and understand their obligation to report them. This behavioral change-from passive observation to active reporting-is the intended outcome of awareness training.
Why A is Wrong: Risk rating changes measure risk management process adjustments rather than individual behavioral change from training. Risk ratings reflect aggregate organizational risk, not the specific behavioral impact of an awareness training program.
Why C is Wrong: Ability to identify financial impacts is a knowledge assessment metric-it measures what users know rather than what they do differently as a result of training. Awareness training aims to change behavior, not just inform.
Why D is Wrong: AI adoption rates measure technology uptake, not risk awareness. Higher adoption could indicate confidence in AI systems but does not measure whether employees recognize and report AI risks- the specific objective of risk awareness training.
NEW QUESTION # 86
Which of the following is the PRIMARY reason to lower AI model temperature?
Answer: A
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 # 87
An organization plans to deploy AI for customer service automation. Which of the following governance approaches BEST aligns culture and risk tolerance?
Answer: B
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. Governance should scale controls to the impact of the customer-service AI system and involve business stakeholders so the control environment reflects risk tolerance and gains operational support. Monitoring alone does not create cultural alignment. This makes option C, Scale controls to AI system impact and involve business stakeholders to obtain buy-in, 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 # 88
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