Isaca ISACA Advanced in AI Security Management (AAISM) Exam AAISM Question # 27 Topic 3 Discussion
AAISM Exam Topic 3 Question 27 Discussion:
Question #: 27
Topic #: 3
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?
A.
Increasing the frequency of model retraining with the existing data set
B.
Applying statistical methods to address missing data and reduce bias
C.
Deleting outlier data points to prevent unusual values impacting the model
D.
Tuning model hyperparameters to increase performance and accuracy
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
[References:, AAISM Study Guide – AI Risk Management (Data Integrity and Quality Controls), ISACA AI Governance Guidance – Data Preparation and Bias Mitigation, ]
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