Your question is Handling Missing Demographics at Scale. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Large customer datasets often have incomplete demographic fields, and the way you handle missing values can materially change downstream segmentation, campaign targeting, and reporting quality.
You are working with millions of customer records used for marketing analysis across surfaces such as Capital One Shopping and card acquisition campaigns. Explain how you would handle a dataset where a significant share of demographic fields is missing. Focus on how you would use SQL to profile missingness, preserve analytical integrity, and prepare the data for downstream use.
Discuss how you would distinguish NULL from other placeholder values, when you would impute versus label values as unknown, how you would structure the logic with CASE WHEN and staged CTEs, and what performance considerations matter when the tables are very large. The interviewer is looking for a practical SQL-oriented approach rather than a purely statistical answer.