Three Airflow DAGs all hit the same Snowflake XL warehouse: a customer-facing daily dashboard DAG, a
A medium Pipeline Design mock interview question on DataDriven. Practice with AI-powered feedback, real code execution, and a hire/no-hire decision.
- Domain
- Pipeline Design
- Difficulty
- medium
Interview Prompt
Three Airflow DAGs all hit the same Snowflake XL warehouse: a customer-facing daily dashboard DAG, a 365-day backfill DAG, and an experimental ML feature build DAG. On Mondays they collide and the daily dashboard misses its 6am SLA. Apply the pools-and-priority framing this section just taught and add structural isolation: split into two orchestrators (one running the customer-facing daily dashboard at high priority on a dedicated path, another running the backfill and ML experimental DAGs at lower priority on isolated compute). The two orchestration nodes together with an observability_tool monitoring the customer-facing path's SLA replace the implicit shared-pool contention with explicit priority isolation that the canvas can model.
How This Interview Works
- Read the vague prompt (just like a real interview)
- Ask clarifying questions to the AI interviewer
- Write your pipeline design solution with real code execution
- Get instant feedback and a hire/no-hire decision