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| Section | Objectives |
|---|---|
| Topic 1: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 2: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 3: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 4: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
ISACA AAIR 덤프로 많은 분들께서 ISACA AAIR시험을 패스하여 자격증을 취득하게 도와드렸지만 저희는 자만하지않고 항상 초심을 잊지않고 더욱더 퍼펙트한ISACA AAIR덤프를 만들기 위해 모든 심여를 기울일것을 약속드립니다.
질문 # 125
Which of the following is the BEST way to integrate AI risk management into operational procedures?
정답：B
설명：
Embedding AI risk management into operations requires that risk assessment activities be integrated throughout the AI development and deployment life cycle, not applied only at discrete checkpoints. This life cycle integration ensures risks are identified and addressed at the stages where they can be most effectively mitigated.
Why C is Correct: The ISACA AAIR curriculum identifies life cycle-integrated risk assessment as the most effective operational integration approach. By introducing risk assessment stages throughout development and deployment-at design, data collection, model training, testing, and deployment-organizations catch risks before they are built into the system. This proactive approach is far more effective than retrospective assessment.
Why A is Wrong: Organization-wide training increases risk awareness but represents an enabler rather than an operational integration mechanism. Training alone does not embed risk practices into workflows.
Why B is Wrong: Third-party audits provide periodic independent assurance but occur infrequently and reactively. They cannot substitute for continuous, integrated risk assessment throughout operations.
Why D is Wrong: Requiring risk committee approval for automation changes creates a governance checkpoint at one decision point. This is narrower than integrating risk assessment across all development and deployment stages and may create bottlenecks without proportionate risk management benefit.
질문 # 126
Which of the following is the PRIMARY benefit of adopting an AI governance framework?
정답：C
설명：
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. An AI governance framework primarily creates structured enterprise oversight, accountability, policy alignment, monitoring, and compliance. Resource allocation and technical standardization may improve, but they are secondary outcomes rather than the central governance benefit. This makes option A, Structured enterprise AI risk oversight and compliance, 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.
질문 # 127
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?
정답：A
설명：
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.
질문 # 128
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
정답：C
설명：
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
질문 # 129
Which of the following is the PRIMARY concern associated with AI-based attacks on systems that store personal data?
정답：D
설명：
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-enabled re-identification is a direct privacy risk because models can combine attributes that appear harmless in isolation and infer an individual identity. This can defeat ordinary masking or pseudonymization and create regulatory exposure even when direct identifiers are absent. This makes option C, AI models may be able to identify combinations of attributes that can be used to re-identify individuals, 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.
질문 # 130
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