A project team is overseeing the data evaluation for an AI model predicting customer churn.

The best answer is A. Using synthetic data generation . PMI’s CPMAI exam outline explicitly includes supervising data augmentation and synthetic data generation as part of managing AI data preparation, and it also highlights the need to address bias, validate data preprocessing results, and ensure the data is suitable before and during model development. When predictions are biased toward a particular class, that usually points to an imbalance or under-representation problem in the training data. Synthetic data generation is an effective mitigation technique because it can increase representation for the weaker class and improve model learning across the full population.

Option B, stratified sampling, is useful for preserving class proportions in train-test splits and for evaluation discipline, but it does not directly correct a class imbalance problem as effectively as targeted synthetic augmentation. Option C affects optimization efficiency, not fairness or class representation. Option D may tune performance, but hyperparameter changes do not address the root issue if the data itself is skewed. PMI’s materials also note that trustworthy AI requires active management of bias, risk, and compliance gaps , which supports selecting a data-centric mitigation approach rather than relying only on model tuning.