Transactions +----+---------+--------+------------+ | id | user_id | amount | date | +----+---------+--------+------------+ | 1 | 101 | 23.50 | 2023-07-01 | | 2 | 102 | 12.99 | 2023-07-01 | | 3 | 101 | 50.00 | 2023-07-02 | | 4 | 103 | 99.95 | 2023-07-02 | | 5 | 101 | 9.99 | 2023-07-03 | +----+---------+--------+------------+ ##### Scenario You have one table that stores all monetary transactions made by users of an e-commerce site. ##### Question For every user, return total_spend rounded to two decimals. 2. Return the user_id(s) that have the second-highest total spend. 3. List users whose transaction_count exceeds the average transaction_count of all users. 4. For each calendar date, calculate revenue_drop_pct compared with the previous day and keep only dates where the drop is ≥10%. 5. For every user, return first_purchase_date together with amount spent on that first day. ##### Hints Window functions, aggregation, self-joins, DATE arithmetic and ROUND will be useful.