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NEW QUESTION: 1
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION: 2
A. Option A
B. Option D
C. Option C
D. Option B
Answer: A
NEW QUESTION: 3
顧客の成功要因を定義するために使用する最良の情報を提供するのはどのQですか？
A. 目標に最も適したシスコ製品はどれですか。
B. このプロジェクト/イニシアチブのビジネス目標は何ですか？
C. どのサービスが必要ですか？
D. ROIをより適切に測定するために、どのようなツールを探していますか？
Answer: B
NEW QUESTION: 4
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?
A. Score Model
B. Train Model
C. Evaluate Model
D. Tune Model Hyperparameters
E. Cross-Validate Model
Answer: E
Explanation:
Explanation/Reference:
Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905852.aspx