Using MongoDB queries, please suggest an answer for the task below

Expected Output:

This is a sample of the database:

{"time": "2019/05/02 00:00:00", "station_id": 150, "station_name": "E 2 St &; Avenue C", "station_status": "In Service", "latitude": 40.7208736, "longitude": -73.98085795, "zipcode": 10009, "borough": "Manhattan", "neighbourhood": "Alphabet City", "available_bikes": 52, "available_docks": 0} {"time": "2019/05/02 00:00:00", "station_id": 2003, "station_name": "1 Ave &; E 18 St", "station_status": "In Service", "latitude": 40.73381219, "longitude": -73.98054421, "zipcode": 10009, "borough": "Manhattan", "neighbourhood": "Stuyvesant Town-Peter Cooper Village", "available_bikes": 50, "available_docks": 4} {"time": "2019/05/02 00:00:00", "station_id": 257, "station_name": "Lispenard St &; Broadway", "station_status": "In Service", "latitude": 40.71939226, "longitude": -74.00247214, "zipcode": 10013, "borough": "Manhattan", "neighbourhood": "Canal Street", "available_bikes": 6, "available_docks": 30} {"time": "2019/05/02 00:00:00", "station_id": 260, "station_name": "Broad St &; Bridge St", "station_status": "In Service", "latitude": 40.70365182, "longitude": -74.01167797, "zipcode": 10004, "borough": "Manhattan", "neighbourhood": "Financial District", "available_bikes": 1, "available_docks": 31} {"time": "2019/05/02 00:00:00", "station_id": 268, "station_name": "Howard St &; Centre St", "station_status": "In Service", "latitude": 40.71910537, "longitude": -73.99973337, "zipcode": 10013, "borough": "Manhattan", "neighbourhood": "Little Italy", "available_bikes": 3, "available_docks": 22} {"time": "2019/05/02 00:00:00", "station_id": 309, "station_name": "Murray St &; West St", "station_status": "In Service", "latitude": 40.7149787, "longitude": -74.013012, "zipcode": 10007, "borough": "Manhattan", "neighbourhood": "Tribeca", "available_bikes": 1, "available_docks": 40} {"time": "2019/05/02 00:00:00", "station_id": 3093, "station_name": "N 6 St &; Bedford Ave", "station_status": "In Service", "latitude": 40.71745169, "longitude": -73.95850939, "zipcode": 11249, "borough": "Brooklyn", "neighbourhood": "Williamsburg", "available_bikes": 0, "available_docks": 26}

I cant load the entire database because of the size.

I think the num_measurements: is a field created to hold the number of instances of that station in the datset.

DATASET 1: This dataset occupies ~50MB and contains 1 file with 183,735 rows. Each row represents a measurement of a CitiBike station and contains the following fields: time , station_id , station_name , station_status , latitude , longitude , zipcode , borough, neighbourhood , available_bikes , available_docks Your task is to: Considering only the measurements where the station_status "In Service", compute the station_id with highest number of measurements having available_bikes = 0. -- DOCUMENT O INFO num_measurements : 2493 station_id : 522 DATASET 1: This dataset occupies ~50MB and contains 1 file with 183,735 rows. Each row represents a measurement of a CitiBike station and contains the following fields: time , station_id , station_name , station_status , latitude , longitude , zipcode , borough, neighbourhood , available_bikes , available_docks Your task is to: Considering only the measurements where the station_status "In Service", compute the station_id with highest number of measurements having available_bikes = 0. -- DOCUMENT O INFO num_measurements : 2493 station_id : 522