最新的ISACA Advanced in AI Audit - AAIA免費考試真題

A. Computational logic is based on probabilities.

B. Servers are reconfigured periodically.

C. Neural node access varies each time the process is executed.

D. Limitations can arise in the quantification of risk profiles.

A. Utilizing unstructured data sources without standardized preprocessing

B. Applying automated anomaly detection without human oversight

C. Training models on historical audit results generated prior to AI adoption

D. Embedding AI audit tools in transactional systems without user training

A. User interface testing

B. Functional testing of the application

C. Model validation on benchmark data

D. Data quality testing

A. Data usage agreements

B. Data performance metrics

C. Use of open-source intellectual property

D. Model runtime efficiency logs

A. Inability to identify where an AI system is housed

B. Difficulty of documenting AI algorithm processes

C. System output not being checked for inconsistencies

D. Cascading failures of AI system outputs

A. Vulnerability assessments

B. Model retraining

C. Data validation

D. Regression testing

A. Standardization of fairness criteria

B. Human oversight and feedback mechanisms

C. Periodic model retraining

D. Data anonymization and classification

A. Submitting the same questions in a foreign language translated by another AI-based system

B. Presenting theoretical situations to justify the reason for asking the questions

C. Asking the same questions later when the algorithm has changed after further learning

D. Randomly placing keywords unrelated to the main topic

A. Review the speed of learning adaptation.

B. Validate the process for handling manipulated inputs.

C. Evaluate the number of system logs maintained.

D. Assess the response time to the attacks.

A. Ensuring vendors implement adequate technical safeguards for data protection

B. Obtaining expressed consent from customers before sharing their data

C. Communicating to customers about AI data sharing practices

D. Including a statement about AI data sharing practices in the company ' s privacy policy

A. Implement robust data validation protocols.

B. Increase training data set size.

C. Rely on external third-party model providers.

D. Use simpler algorithms to improve explainability.

A. Retaining and analyzing all available customer data to ensure unbiased recommendations

B. Utilizing customer purchase data only after obtaining explicit consent and allowing customers to opt out

C. Providing the public with access to review and audit the data set of collected customer information

D. Sharing customer purchase data with third-party vendors to improve advertising and communication

A. Independently verifying audit options based on control assessments

B. Simulating financial transactions for internal control testing

C. Allowing the auditor to replace walkthroughs with automated testing

D. Summarizing multiple process narratives for inconsistencies

A. Limited data set diversity impacting model training

B. Lack of outlier checks in data affecting forecast accuracy

C. Data drift impacting system forecasting

D. Overfitting issues due to a small training data set