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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability |
| Topic 2: AI Risk Program Management | 42% | - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI governance communication and reporting |
| Topic 3: AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks |
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NEW QUESTION # 60
Which of the following is MOST important to verify when assessing open-source AI components in an organization's supply chain?
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. Open-source AI components should be traceable so the organization can verify origin, dependencies, integrity, licensing, and maintenance history. Geography and cost are secondary, and documentation ownership alone does not establish trustworthy provenance. This makes option A, Traceability for open-source AI artifacts, 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 # 61
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: C
Explanation:
Model drift occurs when the statistical relationship between model inputs and outputs changes over time, causing previously accurate predictions to become less reliable. Regular retraining with updated, relevant data recalibrates the model to current real-world patterns.
Why A is Correct: According to ISACA AAIR model maintenance guidance, regular retraining with new relevant datasets is the most direct mitigation for model drift. By periodically retraining on current data, the model learns the latest patterns and relationships-counteracting the drift that accumulates as real-world conditions diverge from the original training data. This is the standard industry practice for maintaining production AI models in dynamic environments.
Why B is Wrong: Restricting automated data validation to low-risk models creates a governance double standard that leaves high-risk models more vulnerable. If anything, high-risk models require more rigorous automated validation, not less. This approach increases rather than mitigates drift risk for critical applications.
Why C is Wrong: Maintaining existing dataset variance during preprocessing preserves statistical characteristics from a historical snapshot. If drift has occurred in real-world data, deliberately maintaining old variance levels prevents the model from adapting to new conditions.
Why D is Wrong: Role-based access controls protect model parameters and data from unauthorized modification. While important for security, access controls do not address model drift, which is driven by changing real-world conditions rather than unauthorized changes.
NEW QUESTION # 62
Which of the following is the BEST course of action to mitigate risk during model selection of supervised or unsupervised algorithms?
Answer: B
Explanation:
Algorithm selection is a foundational risk management decision in AI development. The wrong algorithm for a given use case can produce inaccurate, unreliable, or harmful outputs regardless of the quality of training data or computational resources applied.
Why D is Correct: The ISACA AAIR model development guidance identifies use case alignment as the most critical algorithm selection criterion. Supervised and unsupervised learning are suited to fundamentally different problem types-supervised learning requires labeled training data and learns mappings to known outputs; unsupervised learning discovers patterns in unlabeled data. Selecting algorithms whose capabilities match the use case's structure and objectives prevents systematic performance failures and misapplied AI.
Why A is Wrong: Generalization capability is an important model quality criterion but represents one of many algorithmic properties. Strong generalization on the wrong problem type still produces poor results. Use case alignment precedes generalization as a selection criterion.
Why B is Wrong: Requiring supervised learning for all training projects is an inappropriate blanket policy.
Many valuable use cases-anomaly detection, customer segmentation, exploratory analytics-are better served by unsupervised approaches. Mandating supervised learning prevents optimal use case matching.
Why C is Wrong: Computational cost is a resource management consideration. Optimizing for cost at the expense of use case fit risks deploying inappropriate models that produce unreliable outputs, creating far greater costs through remediation or harm.
NEW QUESTION # 63
Which of the following would be of GREATEST concern to a risk practitioner reviewing the testing and validation of an AI-driven technical support system?
Answer: D
Explanation:
AI-driven technical support systems rely on accurate, current knowledge to resolve user issues. Model drift causes the system to diverge from real-world conditions, producing inaccurate outputs that erode user trust, increase escalations, and potentially cause harm if incorrect technical guidance is followed.
Why A is Correct: According to ISACA AAIR validation guidance, inaccurate outputs from model drift represent the greatest risk in a technical support AI because they directly compromise the system's core function-providing correct technical guidance. Inaccurate outputs lead to unresolved issues, potential system damage from wrong instructions, and reputational harm. Unlike the other options, drift-driven inaccuracy affects every user interaction and cannot be remediated without model updates.
Why B is Correct Context: Infrequent training dataset updates are a contributing cause of model drift and are a serious concern, but they are an input factor rather than the manifest risk itself. The concern is the resulting inaccuracy.
Why C is Wrong: Encryption is a security control for data in storage and transit. While important for confidentiality, it does not affect the accuracy of AI outputs or the system's ability to provide correct technical guidance.
Why D is Wrong: Excessive manual sampling is a testing methodology concern that may reduce testing coverage efficiency. However, it represents a process inefficiency rather than a direct risk to output quality- the model's accuracy is the greater concern.
NEW QUESTION # 64
An organization deploys an autonomous system that makes decisions affecting compliance with regulations.
If those decisions could potentially produce regulatory breaches, which of the following BEST helps to manage associated liability exposures?
Answer: A
Explanation:
Liability from autonomous AI decisions affecting regulatory compliance requires organizations to demonstrate accountability, oversight, and control. Documentation of decision rationale and embedded oversight controls are the primary mechanisms for demonstrating responsible governance to regulators.
Why B is Correct: The ISACA AAIR framework identifies explainability documentation and embedded oversight controls as the key liability management tools for autonomous AI systems. When the organization can demonstrate that each AI decision was explainable, that controls were in place to detect violations, and that human oversight was embedded in the process, this demonstrates due diligence-which is the legal and regulatory standard for managing liability from automated decisions.
Why A is Wrong: Separate compliance programs fragment governance and may increase rather than reduce liability by suggesting AI compliance is siloed from the enterprise compliance program. Regulators expect integrated governance.
Why C is Wrong: Restricting deployment represents risk avoidance, not liability management for already- deployed systems. If the system is already in production, deployment restriction does not address existing liability.
Why D is Wrong: Single-point escalation and non-disclosure create governance bottlenecks and conflict with regulatory transparency requirements. Restricting disclosure cannot be used to shield the organization from regulatory accountability for automated decisions.
NEW QUESTION # 65
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