##### Question You are building a predictive model for a product team. For some users you have address fields such as street, city, state, and ZIP code. Many records include a ZIP code, but some do not. You may also join external public data, such as census-style demographic summaries, using ZIP code or geography. Assume the prediction target is not specified; answer in a general way that would be appropriate for a product-focused data science interview. 1. What address-derived or ZIP-linked features would you consider using as model inputs? What external public datasets could you join on ZIP code or geography to create additional features? 2. How would you encode geographic fields, especially high-cardinality ZIP codes? 3. How would you handle missing ZIP codes? Discuss when to drop vs. impute, hierarchical fallbacks, and missingness as a potentially informative signal. 4. What risks would you watch for when using geographic and demographic variables (fairness, privacy, leakage, overfitting, staleness)? 5. How would you evaluate whether these features actually improve the model?