AAIR 無料問題集「ISACA Advanced in AI Risk」

（A）Return on investment (ROI) for new AI services can be evaluated more accurately.

（B）Ethical principles can be added to AI development and usage after deployment.

（C）The transparency and explainability of AI model decisions is enhanced for all stakeholder groups.

（D）Executive support for technical training and upskilling related to AI can be more effectively obtained.

（A）The system is unable to learn from complex situations not encountered during training.

（B）The system is used to accelerate just-in-time warehouse processes.

（C）The deep neural network used by the system contains datasets with proprietary information.

（D）The organization uses outside contractors to address the lack of in-house AI knowledge.

（A）enhancing predictive capabilities to identify potential disruptions.

（B）performing sentiment analysis on supplier reputation and reviews.

（C）automating routine inventory management tasks.

（D）identifying historical physical and logical security control gaps.

（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）Implementing stringent approval processes for user access to new model functionalities

（B）Expediting rollout of changes in production to ensure service continuity and minimize downtime

（C）Allowing operational teams to adjust configuration parameters for real-time performance tuning

（D）Conducting rigorous validation to assess effects on predictive accuracy and model bias

（A）Elevated residual exposure due to lack of effective controls

（B）Degraded user experience resulting from reduced system performance

（C）Increased operational inefficiency and reliance on manual processes

（D）Deterioration of model accuracy due to restrictions on training datasets

（A）Reduction of human involvement through automation of risk analyses and treatment decisions

（B）Early detection of latent vulnerabilities by identifying anomalous patterns within large datasets

（C）Comprehensive logging and documentation of unauthorized AI system access attempts

（D）Forecasting industry-specific AI risk trends and projecting future financial and business risk

（A）Enhanced alignment of analysis outputs with organizational risk thresholds

（B）Automated production of technical performance reports for AI business tools

（C）Centralized governance of AI inventory and classification activities

（D）Reduced costs through elimination of redundant business risk oversight mechanisms

（A）Business disruption due to delayed remediation of unstable AI behavior

（B）Excessive cost and resource allocation due to redundant mitigation activities

（C）Inadequate representation of AI operational risk in governance reporting

（D）Loss of traceability from insufficient model decision logging

（A）Overfitting to limited datasets

（B）Increased likelihood of exposing proprietary algorithms

（C）Inefficient model training cycles

（D）Presence of unaddressed system vulnerabilities