Assume the following marketplace tables. Table: `listing_interactions` - `buyer_id` STRING - `seller_id` STRING - `product_id` STRING - `interaction_date` DATE - `listing_interactions` INT — number of buyer-seller interactions for that product on that date Table: `dim_all_products` - `product_id` STRING - `category` STRING - `snapshot_date` DATE - `create_date` DATE - `country` STRING Assumptions: - `listing_interactions.product_id` joins to `dim_all_products.product_id`. - Each `product_id` in `dim_all_products` represents one listing/product. - Use all available interaction history unless a question specifies a time filter. - All dates are in UTC. - A product has "multiple interactions" if its total `listing_interactions`, summed across all rows, is greater than 1. - "Created in the last 7 days" means `create_date` is between `current_date - 6 days` and `current_date`, inclusive. Write SQL to answer both questions below: 1. How many sellers have more than 3 distinct products that each have multiple interactions? - Required output: one row with column `sellers_with_gt_3_multi_interaction_products`. 2. Among listings in the US that were created in the last 7 days, what percentage of total listing interactions comes from the `vehicle` category? - Required output: one row with column `vehicle_interaction_pct`. - Define this as: `sum(listing_interactions for US listings created in the last 7 days where category = 'vehicle') / sum(listing_interactions for all US listings created in the last 7 days)`. - Handle divide-by-zero safely if there are no qualifying interactions.