Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
A travel agency named Margie's Travel sells airline tickets to customers in the United States.
Margie's Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure nears about possible delays due to weather conditions. The flight data contains the following attributes:
DepartureDate: The departure date aggregated at a per hour granularity

Carrier: The code assigned by the IATA and commonly used to identify a carrier

OriginAitportID: An identification number assigned by the USDOT to identify a unique airport (the flight's

origin)
DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight's

destination)
DepDel: The departure delay in minutes

DepDel30: A Boolean value indicating whether the departure was delayed by 30 minutes or more (a

value of 1 indicates that the departure was delayed by 30 minutes or more) The weather data contains the following attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SkyConditionVisibility, WeatherType, WindSpeed, StationPressure, PressureChange, and HourlyPrecip.
You have an untrained Azure Machine Learning model that you plan to train to predict flight delays.
You need to assess the variability of the dataset and the reliability of the predictions from the model.
Which module should you use?