Pretty straightforward once you remember to left join from users and filter on the ride side for nulls.
Clarify the definition of 'took zero rides' and the grain of the users table, then use a LEFT JOIN or NOT EXISTS to identify users with no rides in the specified window. Count distinct users who have no matching ride records between July 1 and July 7, 2024.
Pro tip: Explicitly state your assumptions about the users table (e.g., whether it includes all registered users or only active users) and whether the window is based on ride request time or completion time, as these details significantly impact the result.
Confirm the definition of 'zero rides' (e.g., no rides requested or completed), the time window boundaries (inclusive), and the grain of the users table (e.g., one row per user).
Determine the join key between users and rides (likely user_id) and ensure the rides table has a timestamp column to filter by the 7-day window.
Create a subset of rides that occurred between July 1 and July 7, 2024, inclusive, using appropriate date functions.
Use a LEFT JOIN from users to the filtered rides and filter for NULL ride IDs, or use NOT EXISTS / NOT IN to exclude users who have rides in the window.
Count the number of distinct user IDs that satisfy the condition, ensuring no duplicates if the users table has multiple rows per user.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and definitions (e.g., ride rating field, month extraction, handling NULLs). Then write a SQL query that groups rides by calendar month, filters out months with zero rides, computes COUNT(*) and AVG(rating) rounded to two decimals, and orders by month descending. Validate edge cases like NULL ratings and timezone effects.
Pro tip: Mention that you would check for NULL ratings and decide whether to exclude them from the average or treat them as zero—this shows attention to data quality and business impact. Also, confirm whether 'month' should be based on ride start time or end time, as this can affect the grouping.
Ask about the table structure, column names (e.g., ride_id, rating, ride_date), and definitions (e.g., what constitutes a 'ride', how to handle NULL ratings). Confirm the expected output format and sorting order.
Use date functions to extract the calendar month (e.g., DATE_TRUNC('month', ride_date) or EXTRACT(YEAR FROM ...) and EXTRACT(MONTH FROM ...)). Ensure you only consider months with at least one ride, which is naturally handled by GROUP BY.
Apply COUNT(*) for total rides and AVG(rating) for average rating. Use ROUND(AVG(rating), 2) to round to two decimal places. Consider whether to use COUNT(rating) if excluding NULLs.
Order the results by month in descending order (e.g., ORDER BY month DESC). Ensure the month is displayed in a clear format (e.g., 'YYYY-MM').
Check for NULL ratings, timezone conversions, and months with no rides (which should be excluded). Discuss how you would validate the query against a sample dataset.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Similar left join pattern as question one but now you're grouping by city from the users table.
Start by clarifying the data model: identify the user table (with city) and ride table (with user_id and ride timestamp). Use a LEFT JOIN from users to rides to include users with zero rides, then aggregate rides per user and filter for counts of 0 or 1, finally grouping by city and counting distinct users. Alternatively, use a subquery to pre-aggregate ride counts per user before joining to users.
Pro tip: Explicitly state that you're using a LEFT JOIN to preserve users with no rides, and mention that COUNT(DISTINCT user_id) is necessary because a user might appear multiple times if the join is not properly aggregated. This shows attention to data integrity and edge cases.
Identify the relevant tables (e.g., users, rides) and their join keys. Confirm that each user has a city attribute and that rides are linked to users via user_id.
Use a subquery or CTE to count the total number of rides per user, ensuring that users with zero rides are represented (e.g., by left joining users to rides and counting rides).
Apply a filter to keep only users whose ride count is 0 or 1.
Group the filtered results by city and count the distinct user_ids to get the final metric.
Check for potential issues like users with multiple cities, null values, or duplicate ride records, and discuss how to handle them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Break the problem into two stages: first, for each user, identify their active months and rank them chronologically using a window function like DENSE_RANK or ROW_NUMBER. Then, filter to rides in the 1st and 3rd active months, join with city information, and compute the average rating grouped by city.
Pro tip: Clarify how to handle ties or missing months—e.g., if a user has rides in January, March, and April, their active months are Jan (1st), Mar (2nd), Apr (3rd). Also, confirm whether 'rating' is per ride or per user; assume per ride unless specified.
Extract distinct year-month combinations from the rides table for each user, ensuring you capture only months where they had at least one ride.
Use a window function (e.g., DENSE_RANK() OVER (PARTITION BY user_id ORDER BY year_month)) to assign a rank to each active month, skipping gaps.
Select rides that occurred in months where the rank is 1 or 3 for each user.
Join the filtered rides with city information (if not already present) and calculate the average ride rating grouped by city.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.