impressions +---------+---------+----------+----------------+ | user_id | country | category | impression_cnt | +---------+---------+----------+----------------+ | 1 | US | Sports | 120 | | 2 | US | News | 80 | | 3 | CN | Games | 200 | +---------+---------+----------+----------------+ ​ user_activity +---------+-------------+ | user_id | activity_dt | +---------+-------------+ | 1 | 2023-09-01 | | 1 | 2023-09-02 | | 1 | 2023-09-04 | | 2 | 2023-09-01 | | 3 | 2023-09-05 | +---------+-------------+ ​ feature_usage +---------+-------------+-----------+ | user_id | usage_dt | feature | +---------+-------------+-----------+ | 1 | 2023-09-02 | search | | 1 | 2023-09-02 | share | | 1 | 2023-09-02 | save | | 2 | 2023-09-03 | search | | 3 | 2023-09-05 | save | +---------+-------------+-----------+ ##### Scenario A global content platform wants to understand engagement and impressions across countries in the last week. ##### Question Write SQL to (a) return, for every country, the content category with the highest total impressions and its count; (b) compute the weekly heavy-user rate, where a heavy user is one who was active on at least 4 distinct days in the last 7 and, on at least one of those days, used 3 or more distinct features. ##### Hints Join the three tables, use GROUP BY with conditional aggregation, distinct counts for days/features, and window/CTE for ranking the top category per country.