Calls +--------+-----------+------------+---------+----------+ | caller | recipient | ds | call_id | duration | +--------+-----------+------------+---------+----------+ | 123 | 456 | 2019-01-01 | 4325 | 864.4 | | 032 | 789 | 2019-01-01 | 9395 | 263.7 | | 456 | 032 | 2019-01-01 | 0879 | 22.0 | +--------+-----------+------------+---------+----------+ ​ Users +---------+-----------+---------+------------+----------+------------+ | user_id | age_bucket| country | primary_os | dau_flag | ds | +---------+-----------+---------+------------+----------+------------+ | 123 | 25-34 | US | iOS | 1 | 2019-01-01 | | 456 | 35-44 | France | Android | 1 | 2019-01-01 | | 789 | 25-34 | US | iOS | 0 | 2019-01-01 | +---------+-----------+---------+------------+----------+------------+ ##### Scenario You work for a video-calling app and need to report user engagement metrics for different geographies. ##### Question What percentage of users whose country = 'France' were on at least one video call yesterday? What is the total video-call duration divided by the number of daily active users (DAU) in the United States today? ##### Hints Join the calls table with the user profile table; use DISTINCT user counts, date filters like DATE(ds)=CURRENT_DATE-1 or CURRENT_DATE, and aggregate durations in seconds/minutes before dividing.