Updated 70-774 Practice 2019

Exam Code: 70-774 (Practice Exam Latest Test Questions VCE PDF)
Exam Name: Perform Cloud Data Science with Azure Machine Learning (beta)
Certification Provider: Microsoft
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NEW 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:
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.
70-774 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3 https://msdn.microsoft.com/library/azure/038d91b6-c2f2-42a1-9215-1f2c20ed1b40

NEW QUESTION 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are working on an Azure Machine Learning experiment. You have the dataset configured as shown in the following table.
70-774 dumps exhibit
You need to ensure that you can compare the performance of the models and add annotations to the results. Solution: You save the output of the Score Model modules as a combined set, and then use the Project Columns module to select the MAE.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: AExplanation:References:

Explanation:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx

NEW QUESTION 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs. You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following: Solution: You create an endpoint to the Computer vision API. Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

NEW QUESTION 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure ML experiment that contains an intermediate dataset. You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to ARFF module, and then add the Execute R Script module. Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

NEW QUESTION 5
You have an Azure Machine Learning environment. You are evaluating whether to use R code or Python.
Which three actions can you perform by using both R code and Python in the Machine Learning environment? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Preprocess, cleanse, and group data.
  • B. Score a training model.
  • C. Create visualizations.
  • D. Create an untrained model that can be used with the Train Model module.
  • E. Implement feature ranking.

Answer: ABC

NEW QUESTION 6
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.
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services. The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
You plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  • A. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
  • B. Create an Azure HDInsight Hadoop cluster.
  • C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
  • D. Process the files by using Python to store the data to a Hadoop instance.
  • E. Create the Machine learning experiment, and then add an Execute Python Script module.

Answer: BDE

NEW QUESTION 7
You have data about the following:
You need to predict whether a user will like a particular movie. Which Matchbox recommender should you use?

  • A. Item Recommendation
  • B. Related Items
  • C. Rating Prediction
  • D. Related Users

Answer: C

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905970.aspx#RatingPredictionOptions

NEW QUESTION 8
You are building a classification experiment in Azure Machine Learning.
You need to ensure that you can use the Evaluate Model module the experiment.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  • A. Connect the input of the Score Model modules to the output of the Evaluate Model module.
  • B. Connect the input of the Score Model modules to the output of the Train Model modules and the output Split Data modules.
  • C. Connect the output of the Score Model modules to the input of the Evaluate Model module.
  • D. Connect the output of the Score Model modules to the input of the Train Model modules and the input of the Split Data modules.

Answer: AB

NEW QUESTION 9
You plan to use the Data Science Virtual Machine for development, but you are unfamiliar with R scripts. You need to generate R code for an experiment.
Which IDE should you use?

  • A. XgBoost
  • B. Rattle
  • C. Vowpal Wabbit
  • D. R Tools for Visual Studio

Answer: C

Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/provision-vm

NEW QUESTION 10
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure ML experiment that contains an intermediate dataset. You need to explore data from the intermediate dataset by using Jupyter.
Solution: In Azure Mt Studio, you use the Save as dataset option, and then open the output in a new notebook. Does this meet the goal?

  • A. Yes
  • B. No

Answer: A

NEW QUESTION 11
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are working on an Azure Machine Learning experiment. You have the dataset configured as shown in the following table.
70-774 dumps exhibit
You need to ensure that you can compare the performance of the models and add annotations to the results. Solution: You consolidate the output of the Score Model modules by using the Add Rows module, and then
use the Execute R Script module.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: A

Explanation:
References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx

NEW QUESTION 12
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs. You need to ensure that your model supports the following:
Solution: You create an Azure notebook that supports the Microsoft Cognitive Toolkit. Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

NEW QUESTION 13
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 process some data by using Azure Machine Learning Studio. You have an intermediate dataset. The dataset has a column that contains date values stored in a format of MM/DD/YYYY.
ft You need to split the column into three separate columns by year, month, and day.
Which module should you use?

  • A. Edit Metadata
  • B. Normalize Data
  • C. Clean Missing Data
  • D. Import Data
  • E. Execute Python Script
  • F. Clip Values
  • G. Clip Values
  • H. Execute Python Script

Answer: A

NEW QUESTION 14
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 scries.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services. The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You plan to share the Machine Learning workspace with the other users.
You are evaluating whether to assign the User role or the Owner role to several of the users.
Which three actions can be performed by the users who are assigned the User role? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Create, open, modify, and delete datasets.
  • B. Create, open, modify, and delete experiments.
  • C. Invite users to the workspace.
  • D. Delete users from the workspace.
  • E. Create, open, modify, and delete web services.
  • F. Access notebooks.

Answer: CDF

NEW QUESTION 15
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:
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

Explanation:
References:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3

NEW QUESTION 16
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.
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services. The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The columns from the original data source must be retained while running the Machine Learning experiment.
Which module should you add after the web service input module and before the prediction module?

  • A. Edit Metadata
  • B. Import Data
  • C. SMOTE
  • D. Select Columns in Dataset

Answer: D

NEW QUESTION 17
You plan to use Azure Machine Learning to develop a predictive model. You plan to include an Execute Python Script module.
What capability does the module provide?

  • A. Outputting a file to a network location.
  • B. Performing interactive debugging of a Python script.
  • C. Saving the results of a Python script run in a Machine Learning environment to a local file.
  • D. Visualizing univariate and multivariate summaries by using Python code.

Answer: D

Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/execute-python-scripts

NEW QUESTION 18
You work for a company that has retail department stores.
You are developing an Azure Machine Learning experiment to predict seasonal sales. You need to address a model overfitting issue by using the following two solutions:
• Solution 1: Controls the penalty for complexity, which, when successful, prevents overfitting
• Solution 2: Separates model selection from testing, causing a more conservative estimate of generalization Which method should you use for each solution? To answer, drag the appropriate methods to the correct
solutions. Each method 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.
70-774 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
70-774 dumps exhibit

NEW QUESTION 19
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

Answer: C

Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/gallery-experiments

NEW QUESTION 20
You have an Apache Spark cluster in Azure HDinsight. The cluster includes 200 TB in five Apache Hive tables that have multiple foreign key relationships.
You have an Azure Machine Learning model that was built by using SPARK Accelerated Failure Time (AFT) Survival Regression Model (spark-survreg).
You need to prepare the Hive data into a single table as input for the Machine Learning model. The Hive data must be prepared in the least amount of time possible.
What should you use to prepare the data?

  • A. a Hive user-defined function (UDF)
  • B. Spark SQL
  • C. the GPU
  • D. Java Mapreduce jobs

Answer: A

NEW QUESTION 21
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