Design a data model for feature adoption tracking
Tests dimensional modeling for high-volume events so PMs can query Feature A not B without complex SQL. A strong answer uses an event fact table plus a materialized user-feature summary. Red flag: a wide user table with boolean columns per feature.
Tests dimensional modeling and query ergonomics: can you build a schema that lets PMs easily find users who adopted Feature A but not Feature B. A strong answer starts with an append-only event fact table capturing user_id, feature_key, and timestamp, then adds a materialized user-feature summary for fast set operations. It also mentions partitioning by date and indexing on user_id. Red flag: suggesting a sparse user-wide table with boolean feature columns that forces schema migrations on every release and cannot track when usage occurred.
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