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| Section | Objectives |
|---|---|
| Topic 1: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 2: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 3: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 4: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 5: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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NEW QUESTION # 89
Which of the following is the MOST important consideration when selecting an AI governance framework for a civil aviation system?
Answer: C
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. Civil aviation is safety-critical, so governance must preserve qualified human oversight, intervention, escalation, and override capability. Technical simplification, privacy, and centralization are relevant but do not replace human accountability for consequential decisions. This makes option A, Provisions for human oversight, 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 # 90
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?
Answer: B
Explanation:
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.
NEW QUESTION # 91
An organization plans to license an AI model trained on proprietary data to external partners. Which risk should the risk practitioner prioritize?
Answer: A
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. Licensing a model trained on proprietary information creates a material risk that confidential intellectual property may be memorized or surfaced in generated outputs. Encryption during training does not eliminate output leakage, and transparency concerns are secondary to direct IP exposure. This makes option D, Exposure of confidential intellectual property in generated content, 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 # 92
A risk practitioner assesses a new AI system and determines that the risk is within the organization's risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?
Answer: C
Explanation:
Even when an AI system is initially assessed as within risk tolerance, its risk profile evolves as the system encounters new data, the operational environment changes, and model performance drifts. Controls that were effective at deployment may become insufficient as these changes accumulate.
Why C is Correct: The ISACA AAIR operational monitoring guidance identifies continuous monitoring for data and performance drift as the most important mechanism for maintaining control effectiveness over time.
Drift detection provides early warning when the AI system begins behaving differently from its validated state-enabling timely control adjustments before risk tolerance is breached. This is particularly critical because AI systems can degrade gradually in ways not visible without active monitoring.
Why A is Wrong: Framework alignment establishes the control baseline but does not actively verify that controls remain effective as the system evolves. Frameworks provide structure; monitoring provides assurance.
Why B is Wrong: Security and risk awareness training is an important human capability development activity but does not detect technical changes in AI system behavior. Training does not substitute for technical monitoring.
Why D is Wrong: Periodic compliance reviews occur at scheduled intervals and may miss drift that develops between review cycles. Continuous monitoring provides real-time detection that periodic reviews cannot match.
NEW QUESTION # 93
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
Answer: C
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.
Representative sampling combined with explicit bias mitigation addresses both the root cause and manifestation of fairness risk. Label cleanup and synthetic data can help, but neither guarantees representative coverage or equitable outcomes by itself. This makes option D, Representative sampling strategies combined with bias mitigation controls, 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
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