Not every quality check should stop the pipeline
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
Not every quality check should stop the pipeline. The section's rule is per-check: block when running is worse than not running, warn when running is better than not running but still imperfect. Five candidate checks sit on the canvas. A primary-key uniqueness violation, a required-column null spike above 5 percent, and a row count below 50 percent of baseline are blockers (downstream joins are now wrong; the consumer would be misled). A row count at 80 to 90 percent of average and a slight freshness drift are warnings. Pick warn or block by adding an authority annotation to each check's name and routing block-class checks to a paging 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