transaction +-----------+---------+------------+---------+ | trans_id | user_id | trans_date | amount | +-----------+---------+------------+---------+ | 1 | 101 | 2023-01-05 | 200.00 | | 2 | 102 | 2023-01-07 | 150.50 | | 3 | 101 | 2023-02-03 | 75.00 | | 4 | 103 | 2023-02-10 | 50.00 | +-----------+---------+------------+---------+ ​ membership +---------+---------+------------+------------+ | user_id | tier | start_date | end_date | +---------+---------+------------+------------+ | 101 | golden | 2022-12-15 | NULL | | 102 | silver | 2023-01-01 | 2023-02-01 | | 103 | golden | 2023-02-01 | 2023-03-01 | | 104 | golden | 2023-01-20 | NULL | +---------+---------+------------+------------+ ##### Scenario Robinhood interview for an Analytics-Engineer/DS role: product database has transaction and membership tables plus a log of P2P messages. ##### Question SQL: Return every transaction that took place while the user’s membership tier was ‘golden’. end_date is NULL for still-active subscriptions. SQL follow-up: Using a window function (ROW_NUMBER or RANK), pick the first transaction made by each user during any ‘golden’ subscription period. Python: Given a list/DF of messages with sender_id and receiver_id, output the five users who sent the most messages and the five who received the most. ##### Hints Think joins on date ranges, COALESCE(end_date, CURRENT_DATE), window partition by user, Counter/collections for frequency tally.