The multi-filter part is where I slowed down.
Start by clarifying the schema and business definitions (e.g., completed trips, airport pickups, fraudulent riders, active drivers). Then outline a multi-step SQL query using CTEs to filter trips, join tables, aggregate metrics, and apply ranking with tie-breaking rules. Finally, discuss potential edge cases and validation.
Pro tip: Demonstrate awareness of data quality and business context by asking about how fraudulent riders are flagged and whether airport pickups are identified by trip coordinates or a dedicated column. Also, mention that tie-breaking should be deterministic and aligned with business priorities (e.g., higher completion rate first).
Ask clarifying questions about table structures, definitions of 'completed', 'airport pickup', 'active driver', and 'fraudulent rider'. Confirm the time window and tie-breaking rules.
Use CTEs to filter trips to completed airport pickups in the US within the last 28 days, exclude trips by fraudulent riders, and join with drivers to get driver details.
Group by driver and calculate total completed airport pickups, total airport requests (including incomplete), and completion rate (completed/requests).
Use window functions (e.g., ROW_NUMBER or RANK) to order drivers by completed pickups descending, then by completion rate descending, then by driver ID ascending for deterministic ties.
Return the top 10 drivers with all required metrics. Discuss potential edge cases (e.g., drivers with zero requests) and how to validate results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Conditional aggregation with SUM(CASE WHEN ...) for each canceler type.
Use a window function to compute the total requests per airport, then filter to airports with at least 100 total requests. For those airports, calculate the cancellation rate for each canceller type (driver, rider, system) by dividing the count of cancellations by that type by the total requests for the airport.
Pro tip: Clarify whether 'cancellation rate' should be computed as a percentage of total requests or as a percentage of all cancellations; the former is more common in product analytics. Also, ensure you handle NULLs in the canceller type appropriately, as they may represent non-cancelled requests.
Identify the relevant tables and columns: requests, cancellations, canceller type (driver, rider, system), and airport. Define cancellation rate as the proportion of requests that were cancelled by each canceller type.
Use a subquery or window function to compute total requests per airport, then filter to only include airports where this total is >= 100.
For the filtered airports, count the number of cancellations for each canceller type (driver, rider, system). Ensure you only count actual cancellations, not all requests.
Divide the cancellation count for each canceller type by the total requests for that airport to get the cancellation rate. Format as a percentage if needed.
Output the airport, canceller type, and cancellation rate, ordered by airport and canceller type for readability. Consider pivoting if a wide format is preferred.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by identifying the relevant tables and columns for trips, cancellations, and rider information, then filter for US airport pickups within the 28-day window. Aggregate canceled trips per rider, sort descending, and limit to 10, ensuring you handle date ranges and status filters correctly.
Pro tip: Clarify the definition of 'canceled' (e.g., by rider, driver, or system) and whether 'airport pickup' includes both departure and arrival trips; these nuances show attention to metric definitions and prevent misinterpretation.
Locate the trips table with fields like trip_id, rider_id, pickup_location_type, status, and trip_date. Ensure you have a way to identify US locations and airport pickups.
Apply filters: trip_date within the last 28 days, country = 'US', pickup_location_type = 'airport', and status = 'canceled'. Use appropriate date functions based on the schema.
Group by rider_id and count the number of canceled trips. Optionally join with a riders table to get rider names for readability.
Order the aggregated counts in descending order and limit to the top 10 riders. Include tie-breaking logic if necessary (e.g., by rider_id).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.