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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement - AI Risk Identification and Assessment |
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NEW QUESTION # 93
Which of the following is the GREATEST societal risk posed by the dissemination of deepfakes created with AI image generation technology?
Answer: D
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. Deepfakes can fabricate convincing audio, images, or video at scale and therefore create a major societal risk of misinformation, manipulation, and erosion of public trust. IP or privacy harms can occur, but misinformation is the broadest societal exposure. This makes option C, Widespread misinformation, 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 # 94
A financial organization is developing an AI model for credit risk assessment. Which of the following is MOST important to ensure the training data supports accurate and unbiased outcomes?
Answer: A
Explanation:
Credit risk assessment AI models trained on unrepresentative datasets perpetuate and amplify historical financial inequities, producing discriminatory outcomes that violate anti-discrimination laws and harm underrepresented borrowers. Dataset diversity is the primary safeguard against training-data-driven bias.
Why A is Correct: According to ISACA AAIR bias and fairness guidance for financial AI, dataset diversity is the most important factor for supporting accurate and unbiased credit risk outcomes. A diverse dataset that represents the full population of potential borrowers-across demographics, income levels, credit histories, and geographies-enables the model to learn genuine risk relationships rather than proxies for protected characteristics. Without diversity, even technically sophisticated models perpetuate discriminatory patterns from historical data.
Why B is Wrong: Supervised learning is a modeling approach, not a data quality characteristic. The choice of supervised learning is appropriate for credit scoring but does not determine whether the training data is representative or unbiased.
Why C is Wrong: Synthetic data augmentation can supplement real data to address specific gaps but cannot substitute for diversity in the underlying real-world data. Synthetic data derived from biased real data may amplify rather than correct the original bias.
Why D is Wrong: Data normalization is a preprocessing technique that scales numerical features to comparable ranges to improve model convergence. It addresses technical modeling quality but has no effect on the representational diversity or demographic fairness of the dataset.
NEW QUESTION # 95
Which of the following is a risk practitioner's BEST recommendation to establish accountability for AI system outputs and decisions?
Answer: C
Explanation:
Accountability in AI governance requires that specific individuals or roles be clearly designated as responsible for AI system outputs, decisions, and associated risks. Without formal documentation of ownership, accountability gaps emerge.
Why D is Correct: The ISACA AAIR framework emphasizes that accountability must be explicit and documented, with named individuals assigned to own AI outcomes. Formal role assignments create a traceable chain of responsibility that supports auditability, regulatory compliance, and effective escalation when issues arise. Named ownership prevents diffusion of responsibility.
Why A is Wrong: A centralized task force creates collective responsibility, which can dilute individual accountability. Governance bodies support oversight but do not replace individual role ownership for specific outputs.
Why B is Wrong: Continuous monitoring and KPIs are valuable operational controls but represent monitoring mechanisms, not accountability structures. Monitoring detects issues but does not assign responsibility for them.
Why C is Wrong: Resource allocation reviews address investment efficiency rather than accountability for AI decisions and outputs. This is a management activity, not an accountability framework.
NEW QUESTION # 96
Which of the following is the PRIMARY purpose of embedding human-centered values into AI governance frameworks?
Answer: D
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. Embedding human-centered values in AI governance is intended to reduce societal, safety, ethical, and environmental harms and protect the rights and interests of affected stakeholders. Transparency and organizational legitimacy are related benefits but are narrower outcomes. This makes option C, Mitigating societal and environmental risk, 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
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
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. Cleansing and normalization reduce inconsistent, erroneous, or statistically distorted training data that can produce biased or inaccurate model behavior. Their primary value is improving trustworthy inputs rather than lowering compute cost or satisfying privacy law by themselves. This makes option C, Addressing statistical distortions in training data that could cause model bias, 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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