A backend team shipped a new field on the checkout_completed event Tuesday afternoon: discount_code_
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
A backend team shipped a new field on the checkout_completed event Tuesday afternoon: discount_code_applied. The pipeline's loader has been writing to a strict-schema target table for six months and started failing the moment the new field appeared. The section names three reactions a loader can have to an unexpected field (reject, silent drop, accept), and the canonical fix is to land the raw payload in a flexible format so the new shape does not crash the pipeline. Spot the broken loader on the canvas: replace the strict-schema warehouse loader and target with a destination that accepts the variant payload (a JSON or VARIANT column in Snowflake or BigQuery, an Avro-tagged record on a topic backed by a schema registry, or a lakehouse table format whose schema_evolution lets new columns appear without a rewrite). The downstream dashboard must read from the new destination.
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