You are given three tables: ### 1) `impression` Event-level table of user impressions. - `impression_id` BIGINT (PK) - `user_id` BIGINT (FK → `user.user_id`) - `pin_id` BIGINT (FK → `pin_info.pin_id`) - `surface` VARCHAR (e.g., 'home', 'search', 'profile', etc.) - `impression_ts` TIMESTAMP (when the impression happened) - `country_code` VARCHAR (ISO country code; assume it is populated for the event) ### 2) `user` - `user_id` BIGINT (PK) - `created_ts` TIMESTAMP ### 3) `pin_info` - `pin_id` BIGINT (PK) - `category` VARCHAR Assumptions: - Use UTC for date boundaries. - “Today” means `DATE(impression_ts) = CURRENT_DATE`. --- ## Task A For **each country**, find the **pin category** with the **highest number of impressions today**. Requirements: - If there is a tie for the highest impression count, return **all tied categories** for that country. - Output columns: - `country_code` - `category` - `impression_cnt` --- ## Task B For **each country**, compute the **percent of active users** who are **highly active**. Definitions (within the **last 7 days**, inclusive of today): - **Active user**: a user with **≥ 1 impression event**. - **Highly active user**: a user who satisfies **both**: 1) Active on **≥ 4 distinct days** (based on `DATE(impression_ts)`), AND 2) On **at least one day**, used **≥ 3 distinct surfaces** (based on distinct `surface` values that day). Requirements: - Output columns: - `country_code` - `active_users` - `highly_active_users` - `pct_highly_active` (as a decimal or percent; specify which in your answer) - Clearly handle divide-by-zero when a country has 0 active users in the window.