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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
| AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies - Enterprise AI risk program design |
| 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 # 154
Which of the following is the BEST way for a risk practitioner to communicate rapidly evolving changes to the organization ' s risk profile resulting from the use of generative AI?
Answer: D
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. Rapidly changing generative-AI risk is best communicated through concrete real-world incidents and business impacts that stakeholders can understand. Technical root causes or exhaustive lists can obscure the practical consequences that decision-makers need to evaluate. This makes option B, Provide real-world examples of risk impacts from generative AI incidents and breaches, 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 # 155
Which of the following is the GREATEST risk when organizational risk tolerance is not embedded into AI policies?
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. If risk tolerance is not embedded in AI policies, different teams may make inconsistent decisions and permit impacts that exceed enterprise-approved limits. This weakens escalation, treatment, and acceptance decisions across the AI portfolio. This makes option C, Inconsistent governance decisions that fail to mitigate unacceptable impacts, 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 # 156
Which of the following is the MOST important consideration to reduce risk during the development of a large language model (LLM)?
Answer: A
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. Security should be assessed throughout LLM design, development, testing, deployment, and maintenance so vulnerabilities and unsafe behaviors are identified before becoming embedded in production. Threat analysis and staff training are supporting activities within that broader secure lifecycle. This makes option D, Ensuring security is assessed throughout the development life cycle, 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 # 157
A healthcare organization plans to use synthetic records in medical research to help protect patient privacy.
Which of the following is the GREATEST risk associated with using synthetic data to train AI models?
Answer: D
Explanation:
Synthetic data is generated algorithmically to resemble real data while protecting individual privacy.
However, synthetic data generation processes may not perfectly capture the full statistical diversity of real- world populations-particularly rare conditions, edge cases, and underrepresented demographic groups.
Why A is Correct: According to ISACA AAIR data quality guidance for AI, the greatest risk of training on synthetic data is that it may not reflect real-world diversity. In healthcare, this is particularly consequential because AI models trained on non-diverse synthetic data may perform poorly for patient populations not well- represented in the original real data-potentially producing inaccurate diagnoses or treatment recommendations for vulnerable groups, perpetuating health inequities.
Why B is Wrong: While reduced diversity could contribute to increased false negatives in some scenarios, this is a specific manifestation of the broader diversity problem. The root cause-lack of real-world representativeness-is the more fundamental and comprehensive risk.
Why C is Wrong: Regulatory noncompliance from synthetic data use depends on jurisdiction-specific requirements. Many regulations explicitly encourage synthetic data to protect privacy. While compliance must be verified, it is not the greatest inherent risk of synthetic data quality.
Why D is Wrong: Synthetic data generation occurs in controlled internal environments and is not inherently more susceptible to data poisoning than other data types. Poisoning risk is a function of data pipeline controls, not whether data is synthetic or real.
NEW QUESTION # 158
Which of the following is the GREATEST risk when an organization lacks clearly defined accountability mechanisms for AI outputs and decisions?
Answer: B
Explanation:
AI systems make decisions that can affect individuals, organizations, and society. When no individual or function is clearly accountable for those decisions, the organization cannot demonstrate due diligence, remedy harms, or mount a coherent legal defense when challenged.
Why D is Correct: The ISACA AAIR framework identifies legal liability as the greatest organizational risk from absent accountability mechanisms. When AI outputs cause harm-discriminatory lending decisions, unsafe autonomous vehicle actions, inaccurate medical diagnoses-the absence of documented accountability makes it impossible to demonstrate responsible governance to courts, regulators, and affected parties. This creates maximum legal exposure across contract, tort, and regulatory law.
Why A is Wrong: Intellectual property exposure is a significant risk in AI contexts (particularly around training data and model weights) but is not primarily caused by absent accountability mechanisms. IP risk arises from access controls and contractual protections.
Why B is Wrong: Ineffective model training is a technical quality issue. While accountability for model development may influence training quality, ineffective training is not the primary risk from absent accountability for outputs and decisions.
Why C is Wrong: Reduced availability is an operational resilience concern. Accountability gaps do not directly cause availability failures, which are driven by architectural and operational factors.
NEW QUESTION # 159
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