ISACA Advanced in AI Risk - AAIR무료 덤프문제 풀어보기

A. Unauthorized disclosure of sensitive records via insecure external connections

B. Insufficient training datasets due to outdated or limited sample coverage

C. Greater likelihood of bias or inaccuracy in chatbot responses

D. Customer dissatisfaction from operational delays

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. Fine-tune model parameters to accommodate variable inputs.

B. Use synthetic data augmentation to supplement training and testing.

C. Implement continuous real-time quality assurance (QA) processes.

D. Perform post-implementation assessments to identify data integrity issues.

A. To ensure internal trust in the model's reliability before launching AI-driven innovation efforts

B. To determine appropriate access to vendor staff for datasets containing sensitive information

C. To ensure timely detection and mitigation of new system risks that could harm individuals

D. To guarantee that existing availability targets will be achieved following each update

A. Vulnerability patch deployment for an AI system

B. Creation of a new AI risk oversight committee

C. Discovery of discriminatory outputs from an AI system

D. Addition of new metrics tracked by automated monitoring

A. Preserving audit trails

B. Justifying model use cases

C. Providing model transparency

D. Listing technical specifications

A. Legal liability

B. Ineffective model training

C. Intellectual property exposure

D. Reduced availability

A. Data encryption

B. Data loss prevention (DLP)

C. Pseudonymization

D. Role-based access control (RBAC)

A. The transparency and explainability of AI model decisions is enhanced for all stakeholder groups.

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

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

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

A. To reduce energy consumption and environmental impact

B. To mitigate the risk of persistent bias in responses to users

C. To enhance consistency and accuracy of model outputs

D. To diversify ideas and recommendations generated by the model