Câu 9: 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 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?

1. A. Cross-Validate Model — đáp án hiện tại
2. B. Evaluate Model
3. C. Tune Model Hyperparameters
4. D. Train Model
5. E. Score Model