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 of1 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?
A.
OriginAirportID
B.
DepDel
C.
DepDel30
D.
Carrier
E.
DestAirportID
DepDel is correct answer.
1
0
Yes.Because both DepDel and DepDel30 provides same information in different forms.
0
0
B.DepDel
Thank you.
0
0
More 2018 New 70-774 Exam Questions PDF can be viewed at: https://drive.google.com/drive/folders/1WVXCup_qKNm0iitL4rKQ_hsgZd6M_dQD?usp=sharing
0
0
New Questions
1. The weather data contains the following attributes: AirportID, ReadingDate (YYYY/MM/DD HH),
SkyConditionVisibility, WeatherType, WindSpeed, StationPressure, PressureChange, and
HourlyPrecip.
You need to use historical data about on-time flight performance and the weather data to predict
whether the departure of a scheduled flight will be delayed by more than 30 minutes.
Which method should you use?
A. clustering
B. linear regression
C. classification
D. anomaly detection
Answer: C
2
0
New Questions
2. 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. Cross-Validate Model
B. Evaluate Model
C. Tune Model Hyperparameters
D. Train Model
E. Score Model
Answer: A
2
2
Cross-validation is an important technique often used in machine learning to assess both the variability of a dataset and the reliability of any model trained using that data.
0
0