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About this interview
A technical interview on Data Modeling, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Distinguish transactional, periodic snapshot, and accumulating snapshot fact tables
Explain how to model many-to-many relationships (bridge tables, factless fact tables)
Describe how aggregate tables and materialized views improve downstream query performance
Articulate the trade-off between pre-aggregation and on-demand computation in modern warehouses
Topics covered
Fact table basicsFact table typesMany-to-many and factlessCumulative and array-based patternsAggregate tables and materialized viewsPre-aggregation vs on-demand computationAnalytical patternsGrain and design decisions
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In dimensional modeling, what is the fundamental purpose of a fact table, and how does it differ from a dimension table?
“What is an aggregate fact table, and how does it relate to the base-level fact table it summarizes? When would you create one?
“How does the choice of fact table grain affect what analytics questions you can and cannot answer? Walk me through a concrete example where picking the wrong grain created problems.