orders +----------+---------+-------------+------------+-------------+ | order_id | user_id | merchant_id | order_date | order_value | +----------+---------+-------------+------------+-------------+ | 1 | 101 | 9001 | 2023-01-05 | 23.50 | | 2 | 102 | 9002 | 2023-01-05 | 45.20 | | 3 | 101 | 9003 | 2023-01-10 | 12.00 | | 4 | 103 | 9001 | 2023-02-02 | 30.00 | | 5 | 104 | 9002 | 2023-02-09 | 60.75 | +----------+---------+-------------+------------+-------------+ ##### Scenario Food-delivery platform wants to understand user and merchant behavior using order data. ##### Question Calculate the percentage of users who are high-frequency customers (e.g., > N orders within a month). 2. After excluding high-frequency users, compute the total order value for each calendar month. 3. Identify the customer with the single highest order value and return user_id and amount. 4a. List merchants that fall into the bottom revenue quantile (e.g., bottom 25%). 4b. Given an existing query that finds bottom-quantile merchants, modify it per a new prompt supplied during the interview. ##### Hints Use window functions, CTEs, aggregation, and percentile logic; state assumptions clearly.