Clarify the schema and definitions (e.g., completed orders, delivery time, order creation time, parameterization). Then write a SQL query that computes the percentage of completed orders where delivery time exceeds order creation time plus the late threshold, using a subquery or CTE to isolate late orders and aggregate.
Pro tip: Always confirm whether 'completed orders' means delivered orders only, and whether the time window applies to order creation or delivery time. Also, handle edge cases like null delivery times or orders not yet delivered.
Ask about table structure, column names, definitions of 'completed', 'delivered late', and how the time window and threshold are parameterized. Confirm if the window filters order creation or delivery time.
Late means delivery_time > order_creation_time + INTERVAL 'late_threshold_minutes minutes'. Use appropriate date/time functions based on the SQL dialect.
Select orders that are completed (e.g., status = 'completed' or 'delivered') and whose order creation (or delivery) falls within the parameterized time window.
Calculate the ratio of late orders to total completed orders, multiplied by 100. Use conditional aggregation or a subquery with COUNT and division.
Write the final SQL query, ensuring correct handling of NULLs, division by zero, and parameter substitution. Optionally, test with sample data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero-request inclusion is the part that gets you.
Start by clarifying the grain of the data and the definition of 'completed orders' and 'delivery requests'. Then write a query that aggregates requests per order, ensuring orders with zero requests are included via a LEFT JOIN or COALESCE, and finally compute the percentage and average using conditional aggregation.
Pro tip: Explicitly state your assumptions about the data model (e.g., orders table and delivery_requests table) and how you handle NULLs or missing requests. This shows you think about data quality and edge cases, which is crucial for a data scientist role.
Ask clarifying questions about the definition of 'completed orders', 'delivery requests', and 'request_threshold'. Confirm the tables involved and their relationships.
Use a LEFT JOIN from orders to delivery_requests and GROUP BY order_id to count requests per order. Ensure orders with zero requests are included by using COALESCE or COUNT on the joined table.
In an outer query, calculate the percentage of orders with requests >= threshold using conditional aggregation (e.g., SUM(CASE WHEN ...) / COUNT(*)), and compute the average requests per order using AVG(request_count).
Consider orders with no requests, NULLs, and potential duplicates. Validate the query logic with a small sample or mental test.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definitions and edge cases (e.g., completed orders, offer increase, sequential requests) to ensure alignment. Then, outline a step-by-step SQL or Python approach: filter completed orders, order requests by time and request_id, compute lagged offer amounts, identify increases, and aggregate metrics. Finally, discuss how to handle ties, missing data, and validation.
Pro tip: Mention that you would validate the results by manually checking a few orders and considering the impact of outliers on the average percentage increase, possibly using median or trimmed mean as a robustness check.
Confirm what 'completed orders' means (e.g., status = 'completed'), define 'offer increase' precisely, and discuss how to handle ties in timestamps or missing offer amounts.
Filter to completed orders, then for each order, sort requests by time and request_id to establish the sequential order.
Use window functions (e.g., LAG) to compare each request's offer amount to the immediately preceding request; flag rows where the current offer is higher.
Compute the percentage of completed orders with at least one increase, and the average percentage increase across all positive increase events (e.g., (current - previous)/previous * 100).
Check for anomalies, consider the distribution of percentage increases, and discuss potential business implications or next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.