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| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | 21% | - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation |
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement |
| AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Policies, Procedures, and Organizational Training - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
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NEW QUESTION # 163
Which of the following is MOST important when defining responsible roles for integrating third-party AI services into an organization's supply chain?
Answer: A
Explanation:
Third-party AI service integration introduces distributed accountability challenges. When external services are incorporated into organizational supply chains, clearly designated ownership of AI governance and risk is essential to ensure responsibilities do not fall into gaps between internal and external parties.
Why A is Correct: According to ISACA AAIR third-party risk principles, designating AI governance and risk ownership is the foundational requirement for supply chain integration. Without clear internal ownership, third-party AI risks cannot be effectively monitored, escalated, or managed. The designated owner ensures vendor performance is assessed against risk criteria, contractual obligations are enforced, and emerging risks are addressed.
Why B is Wrong: Standardizing data transfer protocols is a technical integration concern. While important for security and operations, it does not address who is responsible for managing the ongoing governance and risk of the integrated service.
Why C is Wrong: Training third-party staff on internal AI policies is a vendor management activity that may be contractually required but does not substitute for internal governance ownership. Third-party training does not remove the organization's responsibility for oversight.
Why D is Wrong: Security policy alignment is important for consistent security posture but represents one dimension of third-party risk management. Governance and risk ownership encompasses security policy alignment as well as broader risk management responsibilities.
NEW QUESTION # 164
Which of the following BEST helps to ensure a deep learning model with a large volume of relevant data meets an organization's needs?
Answer: D
Explanation:
Deep learning models have numerous hyperparameters-learning rate, batch size, regularization parameters, network architecture choices-that control how the model learns from data. Fine-tuning these parameters optimizes model performance for the specific dataset and task requirements.
Why D is Correct: According to ISACA AAIR model development guidance, when a large volume of relevant data is already available, hyperparameter fine-tuning is the most effective technique for ensuring the model meets organizational needs. It systematically optimizes the learning process to maximize performance on the specific problem, calibrating accuracy, generalization, and efficiency to the organization's requirements.
Why A is Wrong: A federated accountability model is a governance structure, not a technical method for optimizing AI performance. It addresses how responsibility is distributed, not how the model learns.
Why B is Wrong: Unsupervised learning is a class of ML approaches used when labeled data is unavailable. It does not address optimization of a deep learning model where relevant data is already present.
Why C is Wrong: Data augmentation artificially expands training datasets through transformations-useful when data is scarce. With a large volume of relevant data already available, augmentation provides minimal additional benefit and hyperparameter optimization becomes the more impactful intervention.
NEW QUESTION # 165
Which of the following poses the GREATEST risk during the development phase of an AI system?
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. Data poisoning can corrupt the learning process before deployment and embed harmful or biased behavior into the resulting model. Because it attacks training-data integrity directly, inadequate poisoning mitigation is a major development-phase risk. This makes option A, Inadequate mitigation of data poisoning attacks, 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 # 166
A risk practitioner is performing a business impact analysis (BIA) of potential service disruptions to an organization's AI-powered operational processes. Which of the following is MOST important to address in the BIA?
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. A business impact analysis focuses on how disruption affects critical operations and on recovery requirements such as recovery time objectives. Development records, asset classification, and incident procedures are related controls but are not the primary purpose of a BIA. This makes option A, Effects of AI system outages on core operations and associated recovery time targets, 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 # 167
An organization seeks to improve AI oversight by incorporating external perspectives regarding potential public harms from AI use. Which of the following is MOST important to consider?
Answer: A
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. External perspectives are best incorporated through formal procedures for engaging affected communities and independent experts. Metrics and post-deployment complaints are useful, but they do not substitute for direct stakeholder participation when assessing public or societal harms. This makes option A, Formal procedures to solicit and prioritize feedback from affected communities and independent experts, 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 # 168
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