Câu 10: 70-774: Perform Cloud Data Science with Azure Machine Learning

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 Margies Travel sells airline tickets to customers in the United States. Margies 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 flights origin) ✑ DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights 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 plan to predict flight delays that are 30 minutes or more. You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?

1. A. OriginAirportID
2. B. DepDel
3. C. DepDel30 — đáp án hiện tại
4. D. Carrier
5. E. DestAirportID