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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| Topic 2: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk monitoring and continuous improvement - AI governance communication and reporting - AI risk assessment and treatment strategies |
| Topic 3: AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI model and data risk identification - AI development, deployment, and monitoring risks |
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NEW QUESTION # 165
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 # 166
Which of the following is the BEST risk response when an organization identifies that ethical and legal exposures from a new non-critical AI system exceeds its risk tolerance?
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. When ethical and legal exposures exceed tolerance and the AI system is non-critical, avoidance is the most proportionate response. Acceptance is inappropriate above tolerance, transfer cannot remove all accountability, and mitigation is suitable only if effective controls can reduce exposure sufficiently. This makes option D, Avoidance, 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 # 167
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 # 168
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: C
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 # 169
Which of the following is the GREATEST risk associated with relying on AI-based analytics for continuous monitoring?
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. AI analytics may misinterpret activity when it lacks contextual understanding, producing false positives or missing meaningful risks. This can directly undermine continuous monitoring decisions, making contextual model error more significant than cost or legacy integration issues. This makes option B, Model errors caused by inability to understand context, 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
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