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| Section | Objectives |
|---|---|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
ISACA試験問題は専門家によって編集され、認定された担当者によって承認され、さまざまな機能を強化するため、AAIRテストトレントを便利かつ効率的に学習できます。購入前に無料ダウンロードと試用を提供しています。 AAIR試験の問題では、学生がシミュレーション問題を提供するプラットフォームで20〜30時間練習する必要があり、ISACA Advanced in AI RiskのAAIR試験に合格する自信を持たせることができます。 AAIR試験に合格して目標を達成するための最良のツールでなければなりません。
質問 # 48
Which of the following BEST mitigates privacy exposure when using datasets for AI model training that include personal information?
正解：D
解説：
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. De- identification directly reduces privacy exposure by removing or transforming identifying information before personal data is used for training. Authentication and human review protect access or oversight but do not remove personal information from the dataset itself. This makes option C, Implementing robust de- identification 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.
質問 # 49
Which of the following is the PRIMARY purpose of AI-driven classification controls such as image recognition or text sentiment analysis?
正解：A
解説：
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.
Classification techniques are designed to assign inputs to defined categories, so their primary operational objective is reliable prediction or categorization. They do not inherently prevent poisoning or determine data quality, and classification is commonly supervised rather than unsupervised. This makes option C, Enhancing predictive accuracy, 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.
質問 # 50
The PRIMARY reason to transfer residual AI risk to a third party is to:
正解：B
解説：
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. Risk transfer is used when a third party assumes defined financial or contractual consequences of residual risk that the organization cannot or chooses not to retain. It can reduce organizational liability, but it does not eliminate the organization ' s governance responsibilities. This makes option A, lower organizational liability when internal mitigations fail, 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.
質問 # 51
An organization has deployed an AI system that initially performs well but whose outputs deteriorate over time despite stable input characteristics. Which of the following is the BEST course of action?
正解：A
解説：
Output deterioration despite stable inputs is a classic indicator of model drift-specifically concept drift, where the underlying relationships between inputs and targets change over time even when the distribution of inputs appears stable. This requires ongoing monitoring and systematic recalibration.
Why D is Correct: The ISACA AAIR life cycle management guidance identifies continuous performance monitoring and scheduled recalibration as the appropriate response to model drift. Monitoring provides early warning when performance degrades below thresholds, while scheduled recalibration ensures the model is periodically updated to reflect current real-world patterns. This systematic approach prevents continued deterioration and maintains model reliability.
Why A is Wrong: Source code audits and peer reviews address development quality and code integrity, not model drift. Drift is a statistical phenomenon driven by changing data relationships, not code defects that code reviews can identify.
Why B is Wrong: Replacing predictive AI with static rule-based systems eliminates the adaptive capabilities that make AI valuable. Static rules cannot respond to evolving patterns and typically perform worse in dynamic environments.
Why C is Wrong: Dataset cleansing addresses data quality for model retraining but does not establish the ongoing monitoring mechanism needed to detect future drift. A one-time cleansing activity cannot prevent recurrent deterioration.
質問 # 52
Which of the following is the PRIMARY benefit of establishing a cross-functional AI governance committee?
正解：A
解説：
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. A cross- functional AI governance committee coordinates legal, privacy, security, business, data, and operational perspectives so AI use is managed consistently across the enterprise. Version control or individual human review are specific activities rather than the committee ' s primary benefit. This makes option D, Coordinated management of AI use 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.
質問 # 53
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