interactions +-----------+----------+------------+----+------------+ | seller_id | buyer_id | product_id | li | create_date| +-----------+----------+------------+----+------------+ | 1 | 101 | 5001 | 5 | 2023-08-01 | | 2 | 102 | 5002 | 12 | 2023-08-03 | | 1 | 103 | 5001 | 4 | 2023-08-04 | | 3 | 101 | 5003 | 2 | 2023-08-05 | | 2 | 104 | 5002 | 7 | 2023-08-07 | +-----------+----------+------------+----+------------+ ​ products +------------+---------+----------+ | product_id | country | category | +------------+---------+----------+ | 5001 | US | validate | | 5002 | CA | search | | 5003 | US | validate | | 5004 | US | explore | | 5005 | FR | validate | +------------+---------+----------+ ##### Scenario An e-commerce marketplace tracks buyer–seller interactions and wants SQL insights using the interactions and products tables. ##### Question How many products have more than 3 distinct buyers and more than 10 total interaction times? What is the percentage of 'validate' interactions for U.S. products in the last 7 days? ##### Hints Use GROUP BY with HAVING, COUNT(DISTINCT buyer_id), SUM(li), date filtering and an INNER JOIN between interactions and products.