Your question is Data Models for Analytics and ML. 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).
How would you architect data models to support both ad-hoc analytics and machine learning feature stores?
Explain how you would separate raw, curated, and serving layers while avoiding duplicated transformation logic. Cover dimensional or wide analytical models, feature definitions, point-in-time correctness, offline and online consistency, schema evolution, lineage, backfills, data quality, and access controls. Identify which technologies you would use for storage, transformation, orchestration, and feature serving, and describe how the design supports reproducible training datasets and reliable production inference.