jobs +--------+---------+-----------+------------+----------------+-------+----------------+---------------+ | job_id | load_id | driver_id | status | delivered_date | miles | payment_issued | rate_per_mile | +--------+---------+-----------+------------+----------------+-------+----------------+---------------+ | 1 | 101 | 501 | completed | 2023-07-01 | 450 | true | 2.2 | | 2 | 102 | 502 | cancelled | 2023-07-03 | 0 | false | 0 | | 3 | 103 | 501 | completed | 2023-07-04 | 600 | true | 1.9 | +--------+---------+-----------+------------+----------------+-------+----------------+---------------+ ​ loads +---------+--------------+-------------------+--------------+ | load_id | origin_state | destination_state | created_date | +---------+--------------+-------------------+--------------+ | 101 | CA | TX | 2023-06-25 | | 102 | NV | AZ | 2023-06-26 | | 103 | OR | WA | 2023-06-27 | +---------+--------------+-------------------+--------------+ ​ drivers +-----------+-----------+-----------+-------+ | driver_id | name | hire_date | state | +-----------+-----------+-----------+-------+ | 501 | Alice | 2022-01-10| CA | | 502 | Bob | 2021-11-05| NV | +-----------+-----------+-----------+-------+ ##### Scenario SQL screen on logistics data – tables jobs, loads, drivers for a trucking marketplace. ##### Question Among the five states that appear most often in the loads table, which state has the highest count of DISTINCT completed jobs? For completed jobs, calculate the average rate_per_mile for each delivered_date (exclude rows where miles = 0). Produce a running cumulative count of completed jobs by delivered_date using a window function (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). ##### Hints Join jobs ↔ loads, filter status = 'completed', be careful with DISTINCT and division by zero, and use window functions correctly.