AAIR 無料問題集「ISACA Advanced in AI Risk」

（A）Reduced AI scalability

（B）Prohibitive migration costs

（C）Lack of transparency

（D）Higher initial expenditures

（A）Conduct parallel model evaluation to quantify the impact of system operations.

（B）Engage external consultants with expertise on measuring broad societal impacts.

（C）Conduct a comprehensive pre-launch evaluation of potential adverse impacts and compliance obligations.

（D）Restrict disclosure of model internal operations to safeguard proprietary algorithms and protect trade secrets.

（A）Improving regulatory compliance posture and readiness

（B）Reducing the frequency of post-deployment monitoring activities

（C）Minimizing risk related to inaccurate or biased decision outputs

（D）Maximizing average-case accuracy on training data

（A）Inconsistent governance decisions that fail to mitigate unacceptable impacts

（B）Insufficient monitoring of accuracy and bias in AI model responses

（C）Inaccurate classification of emerging risk related to AI system vulnerabilities

（D）Overly prescriptive controls inhibiting organizational innovation

（A）Requiring use case approval from senior management for AI applications

（B）Aligning with globally recognized IT policy frameworks and standards

（C）Building trust by embedding AI risk awareness within organizational training

（D）Implementing anonymous reporting channels for AI policy violations

（A）Secure disposal of personally identifiable information (PII)

（B）Revocation of API connections to the model

（C）Encryption of data at rest and in transit

（D）Adherence to industry-specific data retention guidelines

（A）Reliance on conventional third-party security providers for system monitoring

（B）Lack of cross-functional AI incident identification and escalation training

（C）Risk threshold acceptance criteria that do not require 100% decision accuracy

（D）Insufficient scenario-based testing of system failure modes and recovery procedures

（A）Evaluating control effectiveness and costs against potential business losses from unmitigated AI risk

（B）Prioritizing controls based on the complexity and computational requirements of the AI system

（C）Determining control performance baselines and reporting requirements for regulatory compliance

（D）Providing comprehensive AI risk awareness training to security and technical personnel

（A）Measuring intent-classification error rates and refining training datasets

（B）Introducing explainable AI techniques and conducting periodic code reviews

（C）Adopting vendor-recommended thresholds and conducting benchmarking

（D）Calculating precision and recall scores and increasing model temperature

（A）Recommendation of replacement products

（B）Post-defect identification of complex root causes

（C）Dynamic inventories of spare equipment parts

（D）Real-time analysis of sensor monitoring data

（A）Inadequate mitigation of data poisoning attacks

（B）Delayed integration of model explainability capabilities

（C）Insufficient scalability and load testing procedures

（D）Failure to maintain comprehensive model documentation

（A）External regulations and compliance checklists

（B）Ethical risk indicators and reporting

（C）Acceptable use policy and acknowledgment

（D）Cyber threat identification and AI incident handling

（A）Responsibility for anonymization of personal data used for model training

（B）Attachment of obligations for governance structure creation

（C）Assignment of liability for breaches impacting models and training data

（D）Adherence to baseline AI security frameworks