Solve SQL and Python OA tasks

Complete the following two online-assessment tasks. ### Task A — SQL You are given two tables: `customer` - `customer_id` INT - `customer_name` VARCHAR - `purchase_id` VARCHAR `purchase` - `id` VARCHAR - `purchase_price` DECIMAL(10,2) - `purchase_date` DATE `customer.purchase_id` joins to `purchase.id`. Based on the sample in the original prompt, interpret **"the earliest 10 years"** as the historical 10-year window that starts on the minimum `purchase_date` in the `purchase` table and ends just before `MIN(purchase_date) + INTERVAL '10 years'`. Assume `purchase_date` is stored as a `DATE` in UTC. Write a SQL query to return the customer(s) whose `purchase_price` is the highest within that earliest 10-year window. If multiple customers tie for the highest qualifying price, return all of them. Required output columns: - `customer_name` - `highest_purchase_price` ### Task B — Python You are given a pandas DataFrame `students` with schema: - `student_id` INT - `math_score` FLOAT NULL - `english_score` FLOAT NULL - `physics_score` FLOAT NULL Write a Python function that returns the top students after the following processing steps: 1. Remove any student who is missing scores in at least two subjects. 2. For the remaining students, fill each missing subject score with the median of that subject, computed over the remaining non-null rows. 3. Sort the cleaned table by `math_score` descending, then `physics_score` descending. If additional tie-breaking is needed, sort by `student_id` ascending. 4. Return the top 5 rows. If fewer than 5 students remain, return all remaining rows. Return the full cleaned rows for the selected students.