## Problem You work on a Messenger-like app. You want to measure how many **active users in Great Britain (GB) today** have been heavy callers recently. ### Tables Assume two tables (names can vary). #### 1) `calls` One row per call record. - `call_id` (STRING, primary key) - `caller_id` (STRING) - `receiver_id` (STRING) - `date` (STRING) — call date in string format (e.g., `'2025-11-06'`) - `duration` (INT) — call duration (seconds) #### 2) `user_daily_status` One row per user per day describing user attributes and activity. - `user_id` (STRING) - `country` (STRING) - `is_active` (INT) — `1` if the user is active on that date, else `0` - `date` (STRING) — snapshot date in string format (e.g., `'2025-11-06'`) ### Definitions / assumptions - “GB active users today” = users with `country = 'GB'`, `is_active = 1` on **today’s** snapshot date. - “Previous seven days” = the 7-day lookback window **excluding today**, i.e. `[today - 7 days, today - 1 day]`. - A user’s “call records” count includes both: - outgoing calls where the user is the `caller_id`, and - incoming calls where the user is the `receiver_id`. - Count calls as number of call records (e.g., count of `call_id`). ## Task Write a SQL query to compute: - the **percentage** of today’s GB active users who had **more than 50** call records in the previous 7 days. ### Output Return at least: - `as_of_date` - `active_users_today` (denominator) - `active_users_gt_50_calls_last_7d` (numerator) - `pct_active_users_gt_50_calls_last_7d` (You may assume your SQL dialect provides a way to parse the date string into a date type.)