The team replaced the cron chain from the previous section with an Airflow orchestrator and three sm
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
The team replaced the cron chain from the previous section with an Airflow orchestrator and three small Python tasks: extract_orders, clean_orders, and aggregate_orders. The orchestrator and the three task nodes are on the canvas, but no dependency edges have been drawn yet, so Airflow does not know what runs after what. Apply the DAG framing this section just taught. Draw the directed edges so the graph encodes the temporal order: Postgres source feeds extract_orders, extract_orders points to clean_orders, clean_orders points to aggregate_orders, and aggregate_orders writes to the Snowflake daily_orders mart. The Morning dashboard reads from the mart. Every edge points from upstream to downstream. The graph must have no cycles; the section is explicit that an edge pointing back into an earlier task is the failure mode that makes the DAG unschedulable.
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