An IS auditor identified data quality issues as a result of insufficient availability of minority...

When a dataset lacks sufficient examples of a " minority class " (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to " Augment the data through synthesizing " (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain. Relabeling (Option D) does not solve the underlying scarcity of the data needed for diverse representation.