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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement |
| AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Data and Asset Management |
| AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Policies, Procedures, and Organizational Training - AI Organizational Processes and Alignment - AI Trustworthiness, Ethical and Societal Implications - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations |
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155. Frage
Which of the following is the PRIMARY benefit of implementing a formal data disposition process for AI training and operational datasets?
Antwort: A
Begründung:
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 formal data disposition process reduces unnecessary retention of AI training and operational data, thereby lowering the amount of sensitive information exposed to breach and reducing noncompliance with retention or data- minimization obligations. This makes option D, Reducing exposure to breaches and regulatory penalties, 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.
156. Frage
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?
Antwort: D
Begründung:
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.
157. Frage
Which of the following is the MOST significant risk when an organization depends on a single AI vendor ' s proprietary environment?
Antwort: B
Begründung:
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. Dependence on a single proprietary AI environment can create vendor lock-in, making migration technically difficult, operationally disruptive, and expensive. Transparency and scalability are relevant concerns, but prohibitive switching costs are the central dependency risk. This makes option B, Prohibitive migration costs, 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.
158. Frage
An organization seeks to implement a new AI system that uses customer information to create targeted product recommendations. Which of the following is the MOST important consideration to ensure the system complies with regulatory requirements?
Antwort: B
Begründung:
Privacy and data protection regulations worldwide-including GDPR, CCPA, and sector-specific laws- impose strict requirements on the collection, use, and processing of personal information. Customer data used for AI systems must be obtained through lawful means with appropriate consent for the specific processing purpose.
Why A is Correct: According to ISACA AAIR guidance on regulatory compliance, the legal basis for processing personal data is the foundational requirement. An AI system built on data collected without proper consent or legal authorization exposes the organization to regulatory penalties, reputational damage, and forced shutdown of the system. Consent must be specific to the AI use case, not merely generic data collection consent.
Why B is Wrong: Backup and storage protocols address data security and resilience, which are compliance requirements but secondary to the lawfulness of data collection. Securely storing improperly obtained data does not cure the regulatory violation.
Why C is Wrong: Human review of recommendations is a governance safeguard for accuracy and fairness, not a regulatory compliance requirement for data collection. Many regulations do not require human review of recommendation systems.
Why D is Wrong: Supervised learning is a modeling technique that does not address regulatory compliance regarding data sourcing. The training methodology is irrelevant to whether the underlying data was legally obtained.
159. Frage
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
Antwort: B
Begründung:
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.
160. Frage
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