Friendship +--------------+-------------+---------------------+---------------------+ | requester_id | approver_id | request_ts | approval_ts | +--------------+-------------+---------------------+---------------------+ | 1 | 2 | 2023-10-01 10:00:00 | 2023-10-01 12:00:00 | | 3 | 4 | 2023-10-01 11:00:00 | NULL | | 5 | 6 | 2023-10-02 09:00:00 | 2023-10-02 09:05:00 | | 7 | 8 | 2023-10-02 14:00:00 | 2023-10-05 10:00:00 | +--------------+-------------+---------------------+---------------------+ ​ Users +---------+---------+ | user_id | is_spam | +---------+---------+ | 1 | F | | 2 | F | | 3 | T | | 4 | F | | 5 | F | +---------+---------+ ##### Scenario Friend-request system wants to monitor quality and abuse for the past week using Friendship and Users tables. ##### Question Q1. Write an SQL query that returns each of the last 7 calendar days together with the same-day acceptance rate (approvals that occurred on the same date as the request divided by total requests that day). Q2. Write an SQL query that yields the percentage of friendship requests last week that did NOT originate from accounts marked spam = 'T'. Q3. The Users table may be incomplete (new users not yet present). Propose at least one data or query change to make Q1-Q2 robust, and list the key hypotheses and edge cases you would validate when interpreting the results. ##### Hints Think DATE(request_ts)=DATE(approval_ts); left joins to Users; NULL handling, late approvals, missing rows, timezone cut-offs.