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CODE WORKSPACE
Write a query that returns one row per detected order anomaly across duplicate orders, suspicious revenue, and invalid order-state transitions.
Result columns · in this order
order_idanomaly_typeHow to approach it
Break each anomaly type into a named CTE, then union the findings into a stable two-column audit result.
Sample input
| order_id | customer_id | status | gross_revenue | refund_amount |
|---|---|---|---|---|
| 1001 | 10 | placed | 120 | 0 |
| 1001 | 10 | placed | 120 | 0 |
| 1002 | 11 | paid | -25 | 0 |
| 1003 | 12 | refunded | 80 | 90 |
4 rows — all rows shown.
| order_id | event_ts | event_name |
|---|---|---|
| 1004 | 2026-03-01T12:55:00Z | cancelled |
| 1004 | 2026-03-01T12:57:00Z | paid |
| 1005 | 2026-03-01T13:30:00Z | placed |
| 1005 | 2026-03-01T13:31:00Z | paid |
4 rows — all rows shown.
Expected output
| order_id | anomaly_type |
|---|---|
| 1001 | duplicate_order |
| 1002 | negative_or_over_refund |
| 1003 | negative_or_over_refund |
| 1004 | paid_after_cancelled |
4 rows — all rows shown.
Constraints
Treat duplicate order rows, negative gross revenue, refunds larger than gross revenue, and paid events after cancellation as audit findings.
Expected skills
CTE decomposition, row-level data quality checks, event ordering, and making audit output easy to review.
Submit for review to find out what your query gets right, what it gets wrong, and how it compares with the best working query for this exercise.
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