Data and Feature Strategy Questions

Explain how you reason about data and feature choices that drive model outcomes. Cover data requirements and sources, annotation and labeling strategies, quality control, and ethical considerations. Discuss feature engineering and representation choices, approaches to handle class imbalance, scarcity, and noisy labels, and techniques such as data augmentation and synthetic data. Describe how you would validate feature usefulness with ablation studies, cross validation, and robustness checks, and how feature and data decisions interact with model selection and evaluation.