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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks - AI model and data risk identification | |
| Topic 2: 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 3: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability |
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NEW QUESTION # 65
Which of the following is MOST important for a risk practitioner to verify when reviewing the decommissioning process for a customer-facing AI solution?
Answer: B
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.
Decommissioning a customer-facing AI solution must include secure disposal of personally identifiable information that no longer has a valid retention purpose. Encryption and API revocation are useful while assets remain active, but they do not remove end-of-life privacy exposure. This makes option D, Secure disposal of personally identifiable information (PII), 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 # 66
A credit-scoring AI solution exhibits steadily declining accuracy despite unchanged input distributions.
Which of the following should a risk practitioner consider to be the GREATEST risk?
Answer: C
Explanation:
When an AI model's accuracy declines despite stable input distributions, the most likely cause is concept drift-where the underlying relationship between inputs and the target variable changes over time. In credit scoring, this may occur when economic conditions, consumer behavior, or risk patterns shift in ways not captured in the original training data.
Why C is Correct: The ISACA AAIR model drift guidance identifies concept drift as the greatest risk in this scenario because it means the model is making credit decisions based on relationships that no longer hold in the current environment. Faulty credit decisions can lead to incorrect denials of creditworthy applicants, incorrect approvals of high-risk applicants, regulatory violations, financial losses, and harm to individuals- all high-severity consequences for a credit-scoring application.
Why A is Wrong: Technical delays in credit score updates are an operational performance concern. Delays create business friction but do not cause the fundamental accuracy problem described in the scenario.
Why B is Wrong: Underfitting from shortened training cycles is a model development quality issue. The scenario specifies stable input distributions and declining accuracy-characteristic of drift, not underfitting, which would manifest differently.
Why D is Wrong: Increased retraining costs represent a financial efficiency concern. While budgetary impacts are real, they are secondary to the risk of faulty credit decisions affecting individuals and regulatory compliance.
NEW QUESTION # 67
Which of the following BEST helps to ensure the success of an AI risk management plan?
Answer: C
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. A successful AI risk management plan depends on robust governance and ethical oversight that establishes accountability, escalation, decision rights, and coordinated monitoring. Individual security or IP controls address only portions of the overall program. This makes option B, Robust structures for governance and ethical oversight, 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 # 68
Which of the following is the GREATEST risk when an AI model is trained on multiple data sources that differ in update frequency and quality?
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. Training from sources with different update frequencies and quality can introduce conflicting, stale, or incomplete information, leading to inconsistent model outputs and operational decisions. Cost and integration issues are secondary to unreliable decision quality. This makes option A, Inconsistency in model outputs and operational decisions, 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 # 69
Which of the following metrics BEST indicates false positives in an AI model output?
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. Precision is directly affected by false positives because it measures the proportion of predicted positives that are actually positive. Recall is more sensitive to false negatives, F1 combines both dimensions, and overall accuracy can hide a high false-positive rate. This makes option A, Precision, 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 # 70
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