ISACA Advanced in AI Risk - AAIR무료 덤프문제 풀어보기

A. Enhanced interpretability and transparency of model decisions

B. Improved alignment of model outputs and behavior with organizational strategic goals

C. Timely identification of adversarial intrusions and unauthorized parameter changes

D. Automated risk register and vulnerability database updates

A. Dynamic inventories of spare equipment parts

B. Recommendation of replacement products

C. Post-defect identification of complex root causes

D. Real-time analysis of sensor monitoring data

A. Inability to balance classes via oversampling

B. Limited generalization to new and unseen data

C. Lack of labeling and explainability

D. Poor performance on complex datasets

A. Enhanced regularization and training techniques to limit the influence of anomalies

B. Stringent controls over model code and deployment artifacts

C. Data integrity reviews in response to indications that significant model drift has occurred

D. Continuous monitoring and anomaly detection for data ingestion pipelines

A. Engage regular third-party audits of AI process and workflow documentation.

B. Introduce AI risk assessment stages throughout the development and deployment process.

C. Require organization-wide training on AI legal and regulatory requirements.

D. Require AI risk committee approval for changes involving automation of manual tasks.

A. Heightened reliance on manual review resulting in approval bottlenecks and slower response times

B. Decrease in AI adoption rates within business units who are regularly responsible for critical decisions

C. Inability to detect discriminatory or inaccurate outputs that expose the organization to regulatory and reputational risk

D. Increased dependence on external AI service providers for managing AI system risk throughout its life cycle

A. Decentralized approval of changes to AI strategic initiatives

B. Unclear accountability for AI control implementation

C. Operational inefficiency due to reluctance to accept non-critical risk

D. Redundant AI acceptable use policies and guidelines

A. Unsupervised learning

B. Federated accountability model

C. Data augmentation

D. Hyperparameter fine-tuning

A. Amplification of biases in historical data

B. Identification of voter locations

C. Susceptibility to contextual drift

D. Distrust of AI among political interest groups

A. Avoiding noncompliance with regulations for handling sensitive data

B. Ensuring model training processes can be efficiently executed

C. Reducing computational overhead and associated cost

D. Addressing statistical distortions in training data that could cause model bias