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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Trustworthiness, Ethical and Societal Implications |
| AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Design, Development/Procurement, and Documentation - AI Data and Asset Management |
| AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Identification and Assessment |
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NEW QUESTION # 93
An organization has deployed an AI system that initially performs well but whose outputs deteriorate over time despite stable input characteristics. Which of the following is the BEST course of action?
Answer: D
Explanation:
Output deterioration despite stable inputs is a classic indicator of model drift-specifically concept drift, where the underlying relationships between inputs and targets change over time even when the distribution of inputs appears stable. This requires ongoing monitoring and systematic recalibration.
Why D is Correct: The ISACA AAIR life cycle management guidance identifies continuous performance monitoring and scheduled recalibration as the appropriate response to model drift. Monitoring provides early warning when performance degrades below thresholds, while scheduled recalibration ensures the model is periodically updated to reflect current real-world patterns. This systematic approach prevents continued deterioration and maintains model reliability.
Why A is Wrong: Source code audits and peer reviews address development quality and code integrity, not model drift. Drift is a statistical phenomenon driven by changing data relationships, not code defects that code reviews can identify.
Why B is Wrong: Replacing predictive AI with static rule-based systems eliminates the adaptive capabilities that make AI valuable. Static rules cannot respond to evolving patterns and typically perform worse in dynamic environments.
Why C is Wrong: Dataset cleansing addresses data quality for model retraining but does not establish the ongoing monitoring mechanism needed to detect future drift. A one-time cleansing activity cannot prevent recurrent deterioration.
NEW QUESTION # 94
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: 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. 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 # 95
Which of the following MOST effectively reduces bias resulting from variances in free-form answers in the dataset?
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. Free-form data can contain inconsistent formats, scales, terminology, and representations that distort model learning.
Scaling and standardizing inputs reduces unwanted variance and helps prevent irrelevant formatting differences from becoming sources of bias. This makes option C, Scaling and standardizing inputs, 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 # 96
Which of the following is the PRIMARY benefit of retraining an AI model with refreshed data?
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. Retraining with refreshed data allows a model to learn current patterns and reduce errors caused by outdated examples or changing real-world relationships. Hyperparameter tuning and stakeholder feedback are separate activities, while privacy deletion is a governance requirement rather than the main performance benefit. This makes option C, Reducing errors and misclassifications by allowing the model to learn from recent examples, 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 # 97
When identifying AI risk scenarios for a customer service chatbot, which of the following is MOST important for a risk practitioner to evaluate?
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. Risk scenario identification should focus on the severity of potential adverse effects on critical stakeholders and organizational objectives. Technical details such as encryption or dataset volume are controls or implementation factors, while impact drives scenario significance. This makes option C, Severity of adverse effects on critical stakeholders and objectives, 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 # 98
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