Microsoft 70-774 Exam Practice Questions (P. 3)
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Question #11
DRAG DROP -
You have an Execute R Script module that has one input from either a Partition and Sample module or a Web service input module.
You need to preprocess tweets by using R. The solution must meet the following requirements:
✑ Remove digits.
✑ Remove punctuation.
✑ Convert to lowercase.
How should you complete the R code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
You may need to drag the split bar panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
You have an Execute R Script module that has one input from either a Partition and Sample module or a Web service input module.
You need to preprocess tweets by using R. The solution must meet the following requirements:
✑ Remove digits.
✑ Remove punctuation.
✑ Convert to lowercase.
How should you complete the R code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
You may need to drag the split bar panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Question #13
DRAG DROP -
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 need to remove the bias and to identify the columns in the input dataset that have the greatest predictive power.
Which module should you use for each requirement? To answer, drag the appropriate modules to the correct requirements. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
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 need to remove the bias and to identify the columns in the input dataset that have the greatest predictive power.
Which module should you use for each requirement? To answer, drag the appropriate modules to the correct requirements. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer:
References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3 https://msdn.microsoft.com/library/azure/038d91b6-c2f2-42a1-9215-1f2c20ed1b40
References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3 https://msdn.microsoft.com/library/azure/038d91b6-c2f2-42a1-9215-1f2c20ed1b40
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