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| Section | Objectives |
|---|---|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
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NEW QUESTION # 151
A risk practitioner is performing a post-implementation review for an AI system used for credit scoring.
Which of the following is MOST important for the risk practitioner to confirm?
Answer: A
Explanation:
Credit scoring AI systems make high-stakes financial decisions that directly affect individuals' access to credit. Post-implementation review for such systems must confirm that the system performs within ethical, legal, and regulatory boundaries-particularly regarding fairness and explainability.
Why B is Correct: According to ISACA AAIR post-implementation review guidance for high-stakes AI, confirming explainability and fairness is the most critical review element for credit scoring systems. Anti- discrimination laws (Equal Credit Opportunity Act, Fair Housing Act) require that credit decisions be explainable and not discriminatory. Fairness testing detects whether the system produces disparate outcomes across demographic groups, while explainability ensures individual decisions can be justified if challenged.
Why A is Wrong: Access token logging is a security audit trail mechanism. While important for access governance, it does not address the primary regulatory and ethical obligations of a credit scoring system regarding decision quality and fairness.
Why C is Wrong: Stakeholder communication of performance metrics is a governance reporting activity.
Metric communication does not confirm the system is making fair, explainable decisions-it only reports on performance indicators.
Why D is Wrong: User ease of learning and use is a user experience and adoption concern. System usability does not determine whether credit scoring decisions are accurate, fair, or legally compliant-which are the primary post-implementation concerns.
NEW QUESTION # 152
A risk practitioner is developing risk scenarios related to successful data poisoning attacks on an AI model used across the organization. Which of the following is the BEST approach to help ensure the scenarios are relevant?
Answer: B
Explanation:
Risk scenario development in AI requires that scenarios be grounded in organizational context, business processes, and actual threat landscapes. Risk scenarios must reflect the specific systems, data flows, and stakeholder concerns relevant to the organization.
Why D is Correct: According to the ISACA AAIR Study Guide, engaging key stakeholders is the cornerstone of effective risk scenario development. Stakeholders bring domain knowledge, business context, and awareness of operational dependencies that technical practitioners may lack. This collaborative approach ensures scenarios address real-world consequences, organizational risk appetite, and business-critical functions-making them actionable and relevant.
Why A is Wrong: Adversarial testing in a sandbox validates controls but does not by itself produce contextually relevant risk scenarios. It is a technical activity, not a scenario development process.
Why B is Wrong: Peer benchmarking provides useful threat intelligence but cannot replace stakeholder engagement. Industry peer data may not reflect the organization's specific AI architecture or risk tolerance.
Why C is Wrong: Data flow diagrams are useful supporting artifacts but describe technical pathways rather than capturing the organizational and business context required for relevant risk scenarios.
NEW QUESTION # 153
Which of the following is the MOST important consideration when assessing risk for an AI system provided by a third party, as opposed to a non-AI related system?
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. Third-party AI requires greater continuous monitoring because models, data, dependencies, and provider behavior can change over time. Traditional onboarding assessments alone may not detect drift, updates, or newly introduced AI-specific risk. This makes option C, Greater emphasis on continuous monitoring due to the dynamically evolving nature of AI models, 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 # 154
Which of the following is the PRIMARY benefit of retraining an AI model with refreshed data?
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. Retraining with refreshed data allows a model to learn current patterns and reduce errors caused by outdated examples or changing real-world relationships. Hyperparameter tuning and stakeholder feedback are separate activities, while privacy deletion is a governance requirement rather than the main performance benefit. This makes option C, Reducing errors and misclassifications by allowing the model to learn from recent examples, 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 # 155
A risk practitioner is assessing risk in a newly implemented AI system integrated into an organization's business processes. Which of the following is the MOST important consideration for the risk practitioner?
Answer: D
Explanation:
AI risk assessment must be calibrated to the potential consequences of AI-driven decisions. The criticality and impact of AI-driven decisions directly determine the magnitude of risk exposure and the appropriate level of risk treatment.
Why D is Correct: According to ISACA AAIR principles, the most fundamental risk assessment consideration is the nature and impact of decisions driven by the AI system. Systems making high-stakes decisions-affecting employment, credit, healthcare, or public safety-carry significantly greater risk than those supporting low-impact tasks. Understanding decision criticality frames all other risk assessment activities and drives proportionate control selection.
Why A is Wrong: Escalation protocols are governance process elements that should be designed after understanding the risk profile. They are outputs of risk assessment, not inputs to the primary assessment consideration.
Why B is Wrong: Prior automation levels provide contextual background but do not determine the risk profile of the new AI system. The relevant risk driver is forward-looking, not historical.
Why C is Wrong: Internal expertise levels affect assessment capability but represent an organizational constraint rather than the primary risk consideration. The risk lies in the system's potential impact, not in who assesses it.
NEW QUESTION # 156
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