덤프는 구체적인 업데이트주기가 존재하지 않습니다. 하지만 저희는 수시로 ISACA AAIR 시험문제 변경을 체크하여ISACA AAIR덤프를 가장 최신버전으로 업데이트하도록 최선을 다하고 있습니다. ISACA AAIR덤프를 구매하면 1년간 업데이트될떼마다 최신버전을 구매시 사용한 메일로 전송해드립니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations |
| Topic 2: 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 |
| Topic 3: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Data and Asset Management |
많은 사이트에서 ISACA인증 AAIR시험대비덤프를 제공해드리는데Itexamdump를 최강 추천합니다. Itexamdump의ISACA인증 AAIR덤프에는 실제시험문제의 기출문제와 예상문제가 수록되어있어 그 품질 하나 끝내줍니다.적중율 좋고 가격저렴한 고품질 덤프는Itexamdump에 있습니다.
질문 # 64
Which of the following is the PRIMARY reason to lower AI model temperature?
정답：C
설명：
Temperature is a hyperparameter in language model generation that controls output randomness. Lower temperatures make the model more deterministic-concentrating probability mass on the most likely tokens and producing more consistent, predictable outputs. Higher temperatures introduce more randomness and diversity.
Why B is Correct: According to ISACA AAIR model configuration guidance, lowering model temperature is primarily used to enhance consistency and accuracy of outputs. In production applications requiring reliable, reproducible responses-such as customer service, compliance reporting, or technical documentation-lower temperature ensures the model consistently generates the most appropriate response based on its learned knowledge, reducing variability and improving output quality.
Why A is Wrong: Temperature adjustment does not directly mitigate bias. Bias in AI models is a function of training data and model architecture, not output randomness. A biased model at low temperature will consistently generate biased outputs; lowering temperature may actually make bias more persistent by reducing variation.
Why C is Wrong: Diversifying ideas and recommendations is achieved by increasing temperature, not lowering it. Higher temperature is used for creative tasks where variety is valuable; lower temperature is used for tasks requiring precision and consistency.
Why D is Wrong: Model temperature has no direct relationship to computational energy consumption. Energy use is primarily driven by model size, computation requirements, and inference frequency-not the temperature parameter.
질문 # 65
An organization uses multiple external data sources to train its AI models. Which of the following is the risk practitioner's BEST recommendation to protect the organization from data poisoning attacks?
정답：D
설명：
Data poisoning attacks involve malicious modification of training data to degrade model performance or introduce backdoors. With multiple external data sources, the attack surface for introducing poisoned data is broad and requires proactive, continuous detection at the ingestion stage.
Why B is Correct: The ISACA AAIR adversarial AI guidance identifies continuous monitoring and anomaly detection at the data ingestion pipeline as the most effective defense against data poisoning. By monitoring incoming data in real time for statistical anomalies, unexpected distributions, or known poisoning patterns, organizations can detect and block malicious data before it contaminates training datasets. This preventive approach is superior to reactive detection after poisoning has occurred.
Why A is Wrong: Reactive data integrity reviews triggered by model drift occur after poisoning has already affected model behavior. By this stage, the model may have been deployed and made harmful decisions.
Prevention during ingestion is superior to post-drift investigation.
Why C is Wrong: Model code and deployment artifact controls address security of the software pipeline but do not protect training data from external poisoning. Data integrity requires data-layer controls, not code security.
Why D is Wrong: Regularization reduces overfitting to training noise but does not detect or prevent deliberate poisoning attacks. A sufficiently targeted poisoning attack can introduce systematic bias that regularization techniques cannot mitigate.
질문 # 66
Which of the following metrics BEST indicates false positives in an AI model output?
정답：A
설명：
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. Precision is directly affected by false positives because it measures the proportion of predicted positives that are actually positive. Recall is more sensitive to false negatives, F1 combines both dimensions, and overall accuracy can hide a high false-positive rate. This makes option A, Precision, 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 # 67
Which of the following is the risk practitioner ' s FIRST course of action upon learning that an AI model has been providing responses that are outside predefined thresholds for accuracy?
정답：C
설명：
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. A breach of a predefined accuracy threshold is a governance event, so the first action is to notify the designated AI governance owner with decision authority. Technical investigation and risk-register updates can follow once the accountable owner has been informed. This makes option C, Notify designated AI governance owners for decision making, 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 # 68
An organization embeds AI into existing processes without integrating AI risk practices into enterprise governance. Which of the following should a risk practitioner regard as the GREATEST organizational risk?
정답：C
설명：
When AI is deployed without governance integration, no formal structure exists to assign control ownership, coordinate risk management activities, or align AI decision-making with organizational objectives. This structural void produces divergent, fragmented, and potentially conflicting risk management efforts.
Why C is Correct: According to ISACA AAIR, unclear ownership is the greatest organizational risk from AI operating outside governance structures. Without designated owners, controls may be applied inconsistently across business units, different teams may implement conflicting approaches, and no one is responsible for ensuring AI activities align with enterprise objectives. This governance vacuum creates unmanaged risks and organizational incoherence.
Why A is Wrong: Regulatory compliance documentation gaps are significant but are a downstream symptom of poor governance rather than the root organizational risk. Documentation failures can be remediated more easily than fundamental ownership gaps.
Why B is Wrong: Technical-business alignment is an important concern but represents a strategic planning challenge rather than the greatest organizational risk from absent governance. Alignment can be achieved through business case processes without full governance integration.
Why D is Wrong: Executive approval difficulty is an organizational change management challenge. It reflects organizational politics rather than a structural risk from absent governance. Approval processes function independently of AI governance integration.
질문 # 69
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