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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Model Training, Testing, and Validation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations |
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NEW QUESTION # 172
Which of the following is the MOST important consideration for a risk practitioner assessing the reproducibility of model outputs?
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.
Reproducibility requires an end-to-end record of metadata and data lineage so the organization can reconstruct data sources, transformations, model versions, and processing conditions. Accuracy KPIs measure performance but cannot recreate how an output was produced. This makes option D, Automated end-to-end capture of metadata and data lineage, 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 # 173
Which of the following is the risk practitioner ' s FIRST course of action upon learning that an AI model has been providing responses that are outside predefined thresholds for accuracy?
Answer: B
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 breach of a predefined accuracy threshold is a governance event, so the first action is to notify the designated AI governance owner with decision authority. Technical investigation and risk-register updates can follow once the accountable owner has been informed. This makes option C, Notify designated AI governance owners for decision making, 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 # 174
Which of the following is the BEST governance approach for balancing risk management and operational flexibility across diverse AI applications?
Answer: B
Explanation:
AI governance across diverse applications requires frameworks flexible enough to accommodate varying risk profiles, regulatory environments, and operational contexts while maintaining consistent governance standards. Rigid or overly centralized approaches reduce operational effectiveness.
Why B is Correct: The ISACA AAIR framework advocates for adaptable governance frameworks that can be scaled and tailored to specific AI use cases. A risk-based, adaptable framework applies more rigorous controls to high-risk applications while allowing operational flexibility for lower-risk uses. This balance enables innovation while maintaining appropriate risk oversight-a core principle of proportionate AI governance.
Why A is Wrong: Single-regulation compliance focus creates compliance tunnel vision that may miss material risks not covered by that regulation. AI governance must address the full risk landscape, not just one regulatory framework.
Why C is Wrong: External consultants provide periodic independent assurance, not governance. Relying on external reviews for governance would be episodic rather than continuous, creating governance gaps between review cycles.
Why D is Wrong: Centralized decision-making creates operational bottlenecks and slows AI deployment.
Effective governance delegates decision authority appropriately while maintaining oversight, rather than centralizing all decisions in a single function.
NEW QUESTION # 175
A risk practitioner assesses an AI model used for predictive diagnostics and finds that the system lacks automated update mechanisms. Which of the following is the GREATEST risk?
Answer: D
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. A predictive diagnostic model that cannot update may continue using assumptions that no longer reflect current conditions, compromising accuracy as the environment changes. Human effort is secondary to the risk of incorrect predictions based on stale relationships. This makes option D, Compromise of predictive accuracy due to outdated model assumptions, 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 # 176
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
Answer: B
Explanation:
Different AI model architectures are optimized for different tasks. Content creation requires a model that can generate novel outputs-text, images, audio, or code-rather than classify, cluster, or optimize decisions based on rules or rewards.
Why A is Correct: According to ISACA AAIR AI technology selection guidance, generative models are specifically designed to synthesize new content by learning the underlying probability distributions of training data. They can produce novel, contextually appropriate outputs-exactly what content creation requires.
Large language models (LLMs), diffusion models, and GANs are generative architectures designed for this purpose.
Why B is Wrong: Unsupervised clustering groups existing data points by similarity but does not generate new content. It is used for pattern discovery and segmentation, not creative output generation.
Why C is Wrong: Rule-based expert systems execute predefined logic trees and cannot produce novel content beyond the rules explicitly encoded. They are rigid, deterministic systems unsuitable for open-ended content creation.
Why D is Wrong: Reinforcement learning optimizes decision sequences to maximize cumulative rewards. It is suited for sequential decision-making tasks (games, robotics, recommendation systems) but is not the appropriate architecture for direct content generation.
NEW QUESTION # 177
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