Data Engineering
Orchestration (Airflow & dbt)
6 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- When would you make a dbt model incremental instead of a view or full table rebuild, and how do you keep an incremental model correct when data arrives late or the logic changes?
- You're building an Airflow DAG that ingests from several upstream sources and a teammate wants each task to just pull 'the latest data' on whatever schedule it runs. Why is that risky, and how would you design the DAG so reruns and backfills are safe?Go Pro
- In Airflow, why does it matter that a DAG's tasks are idempotent and parameterized by the run's logical date rather than by the current date?Go Pro
- Your dbt project's transformations are all full-refresh table rebuilds, and as data grows the runtime and warehouse cost are climbing. How would you reason about moving the heavy models to incremental, and what correctness traps would you watch for?Go Pro
- What does it mean for an Airflow DAG to be idempotent, and how do you design tasks so they're safe to retry or backfill without corrupting the target data?Go Pro
- In dbt, what's the difference between a view, a table, and an incremental materialization, and how would you choose between them for a model?Go Pro
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