An e-commerce checkout pipeline writes order_amount_usd that is normally distributed around a mean o
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
An e-commerce checkout pipeline writes order_amount_usd that is normally distributed around a mean of 84 dollars with a standard deviation near 32. One Wednesday the mean drops to 71. No row is invalid; every value is plausible. Row counts are normal, nulls absent, uniqueness preserved, schema intact. The cause turns out to be a coupon-stacking bug applying twice for one customer segment. The section's pattern: distributional checks compare summary statistics (mean, stddev, quantiles, category mix) of the current run against a rolling historical baseline, and fire when the comparison crosses a z-score or percentage-point threshold. Detect the drift by adding a distributional-check transform whose name states the statistic, the baseline window, and the threshold.
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