Download Microsoft.Passguide.70-774.2017-12-06.1e.17q.vcex

Microsoft.Passguide.70-774.2017-12-06.1e.17q.vcexMicrosoft.Passguide.70-774.2017-12-06.1e.17q.vcexMicrosoft.Passguide.70-774.2017-12-06.1e.17q.vcexMicrosoft.Passguide.70-774.2017-12-06.1e.17q.vcexMicrosoft.Passguide.70-774.2017-12-06.1e.17q.vcexMicrosoft.Passguide.70-774.2017-12-06.1e.17q.vcex

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Exam Perform Cloud Data Science with Azure Machine Learning
Number 70-774
File Name Microsoft.Passguide.70-774.2017-12-06.1e.17q.zip
Size 662 Kb
Posted January 05, 2018

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Demo Questions

Question 1

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 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.


Question 2

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: Cross-Validate Model
  • B: Evaluate Model 
  • C: Tune Model Hyperparameters
  • D: Train Model
  • E: Score Model
Question 3

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 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
Question 4

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question. 
You need to remove rows that have an empty value in a specific column. The solution must use a native module. 
Which module should you use?


  • A: Execute Python Script
  • B: Tune Model Hyperparameters
  • C: Normalize Data
  • D: Select Columns in Dataset
  • E: Import Data
  • F: Edit Metadata
  • G: Clip Values
  • H: Clean Missing Data
Question 5

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question. 
You have a non-tabular file that is saved in Azure Blob storage. 
You need to download the file locally, access the data in the file, and then format the data as a dataset. 
Which module should you use?


  • A: Execute Python Script
  • B: Tune Model Hyperparameters
  • C: Normalize Data
  • D: Select Columns in Dataset
  • E: Import Data
  • F: Edit Metadata
  • G: Clip Values
  • H: Clean Missing Data
Question 6

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question. 
You have a dataset that contains a column named Column1. Column1 is empty. 
You need to omit Column1 from the dataset. The solution must use a native module. 
Which module should you use? 


  • A: Execute Python Script
  • B: Tune Model Hyperparameters
  • C: Normalize Data
  • D: Select Columns in Dataset
  • E: Import Data
  • F: Edit Metadata
  • G: Clip Values
  • H: Clean Missing Data
Question 7

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question. 
You need to use only one percent of an Apache Hive data table by conducting random sampling by groups. 
Which module should you use?


  • A: Execute Python Script
  • B: Tune Model Hyperparameters
  • C: Normalize Data
  • D: Select Columns in Dataset 
  • E: Import Data
  • F: Edit Metadata
  • G: Clip Values
  • H: Clean Missing Data
Question 8

From the Cortana Intelligence Gallery, you deploy a solution. 
You need to modify the solution. 
What should you use?


  • A: Azure Stream Analytics
  • B: Microsoft Power BI Desktop
  • C: Azure Machine Learning Studio
  • D: R Tools for Visual Studio
Question 9

You are building an Azure Machine Learning workflow by using Azure Machine Learning Studio. 
You create an Azure notebook that supports the Microsoft Cognitive Toolkit. 
You need to ensure that the stochastic gradient descent (SGD) configuration maximizes the samples per second and supports parallel modeling that is managed by a parameter server. 
Which SGD algorithm should you use?


  • A: DataParallelASGD
  • B: DataParallelSGD
  • C: ModelAveragingSGD
  • D: BlockMomentumSGD
Question 10

You are analyzing taxi trips in New York City. You leverage the Azure Data Factory to create data pipelines and to orchestrate data movement. 
You plan to develop a predictive model for 170 million rows (37 GB) of raw data in Apache Hive by using Microsoft R Server to identify which factors contribute to the passenger tipping behavior. 
All of the platforms that are used for the analysis are the same. Each worker node has eight processor cores and 26 GB of memory. 
Which type of Azure HDInsight cluster should you use to produce results as quickly as possible?


  • A: Hadoop
  • B: HBase
  • C: Interactive Hive
  • D: Spark



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