You are given three tables. Assume all timestamps are stored in UTC. - `users(user_id BIGINT PRIMARY KEY, create_date TIMESTAMP)`: one row per user account. - `transactions(transaction_id BIGINT PRIMARY KEY, user_id BIGINT, product VARCHAR, amount_gbp DECIMAL(12,2), status VARCHAR, transaction_time TIMESTAMP)`: one row per transaction. `status` is either `'completed'` or `'declined'`. - `activity(user_id BIGINT, product VARCHAR, event_type VARCHAR, event_time TIMESTAMP)`: one row per product interaction. `event_type` is either `'view'` or `'click'`. Write SQL for both tasks below: 1. **High-value crypto users in the first 7 days after signup** - For each user, use the window `[create_date, create_date + INTERVAL '7 day')`. - Only count rows from `transactions` where `product = 'crypto'` and `status = 'completed'`. - Return users whose total crypto transaction amount in that 7-day window is greater than £100. - Required output columns: `user_id`, `total_crypto_amount_7d`. 2. **Product with the highest CTR** - Compute CTR by product from `activity`, where: - `clicks = COUNT(*)` for `event_type = 'click'` - `views = COUNT(*)` for `event_type = 'view'` - `ctr = clicks / views` - Exclude products with zero views. - Return the product or products with the highest CTR. - Required output columns: `product`, `views`, `clicks`, `ctr`.