CALL_LOGS | call_id | user_id | call_start | call_end | is_group_call | participant_cnt | | 101 | 12 | 2023-08-01 10:00:00 | 2023-08-01 10:15:00 | 0 | 2 | | 102 | 34 | 2023-08-01 10:05:00 | 2023-08-01 10:30:00 | 1 | 4 | | 103 | 56 | 2023-08-01 11:00:00 | 2023-08-01 11:20:00 | 1 | 3 | | 104 | 12 | 2023-08-02 09:00:00 | 2023-08-02 09:10:00 | 0 | 2 | | 105 | 78 | 2023-08-02 09:15:00 | 2023-08-02 09:40:00 | 1 | 5 | ##### Scenario A messaging app is launching a new "group call" feature. You have access to historical call data and need to quantify early adoption of the feature. ##### Question Using the call_logs table, write SQL to: For each calendar day, return (a) total calls, (b) total group calls, (c) unique callers who initiated a group call, and (d) percentage of calls that are group calls. List all days where group-call percentage is below 10%. ##### Hints DATE(call_start), COUNT(*), SUM(is_group_call), COUNT(DISTINCT CASE WHEN is_group_call=1 THEN user_id END)