AAISM Practice Test

AAISM Practice Test practice questions, page 10 of 12, with answer review and explanations.

Question 46

Which of the following controls BEST mitigates the risk of bias in AI models?

  1. Regular data reconciliation
  2. Diverse data sourcing strategies
  3. Robust access control techniques
  4. Cryptographic hash functions

Question 47

Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?

  1. Implementing an automated data cataloging tool that integrates with all organizational data repositories
  2. Creating a centralized team to oversee the classification of data used in AI projects
  3. Conducting quarterly audits of AI data sets for anomalies and missing metadata
  4. Establishing a manual process to categorize data based on business needs and regulatory compliance

Question 48

An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data. Which of the following is the MOST effective data cleaning technique to improve model performance?

  1. Applying statistical methods to address missing data and reduce bias
  2. Increasing the frequency of model retraining with the existing data set
  3. Tuning model hyperparameters to increase performance and accuracy
  4. Deleting outlier data points to prevent unusual values impacting the model

Question 49

Which of the following BEST describes the role of risk documentation in an AI governance program?

  1. Offering detailed analyses of technical risk and vulnerabilities
  2. Demonstrating governance, risk, and compliance (GRC) for external stakeholders
  3. Outlining the acceptable levels of risk for AI-related initiatives
  4. Providing a record of past AI-related incidents for audits

Question 50

Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?

  1. Weak controls for access to the AI model
  2. Lack of protection against denial of service (DoS) attacks
  3. Inaccurate generalizations from new data by the AI model
  4. Inability to detect input modifications causing inappropriate AI outputs