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NEW QUESTION 1
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 web service input to retrieve the data for the data source, and then add the Execute R Script module.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

NEW QUESTION 2
You need to identify which columns are more predictive by using a statistical method. Which module should you use?

  • A. Filter Based Feature Selection
  • B. Principal Component Analysis
  • C. Group Data into Bins
  • D. Tune Model Hyperparameters

Answer: B

NEW QUESTION 3
You have an Azure Machine Learning experiment.
You discover that a model causes many errors in a production dataset. The model causes only few errors in the training data.
What is the cause of the errors?

  • A. overfitting
  • B. generalization
  • C. underfitting
  • D. a simple predictor

Answer: A

NEW QUESTION 4
You plan to use the Import Data module to import data from a web URL by using HTTP. Which file format can you use as the source of the import operation?

  • A. Optimized Row Columnar (ORQ
  • B. Extensible Markup Language (XML)
  • C. comma-separated value (CSV)
  • D. JavaScript Object Notation (JSON)

Answer: D

NEW QUESTION 5
You have data about the following:
• Users
• Movies
• User ratings of the movies
You need to predict whether a user will like a particular movie. Which Matchbox recommender should you use?

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

Answer: A

NEW QUESTION 6
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

Answer: C

NEW QUESTION 7
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 CSV module to the Azure ML experiment and then open the module output in a new notebook.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: A

NEW QUESTION 8
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 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

Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905852.aspx

NEW QUESTION 9
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 finish training the model and are ready to publish a predictive web service that will provide the users with the ability to specify the data source and the save location of the results. The model includes a Split Data module.
Which two actions should you perform to convert the Machine Learning experiment to a predictive web service? To answer, drag the appropriate actions to the correct targets. Each action 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

    Answer:

    Explanation: References:
    https://docs.microsoft.com/en-us/azure/machine-learning/studio/convert-training-experiment-to-scoring-experim

    NEW QUESTION 10
    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

      Answer:

      Explanation: 70-774 dumps exhibit

      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 connect the Score Model modules from each trained model as inputs for the Evaluate Model
      module, and then save the results as a dataset.
      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
      You have the following three training datasets for a restaurant:
      You must recommend restaurant to a particular user based only on the users features. You need to use a Matchbox Recommender to make recommendations.
      How many input parameters should you specify?

      • A. 1
      • B. 2
      • C. 3
      • D. 4

      Answer: B

      Explanation: References:
      https://msdn.microsoft.com/en-us/library/azure/dn905987.aspx

      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 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

      Answer: A

      Explanation: References:
      https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/sample-data-hive

      NEW QUESTION 14
      You deploy Microsoft Data Management Gateway.
      You plan to use the Import Data module in Azure Machine Learning Studio to import data from an on-premises Microsoft SQL Server instance.
      Which operation can you perform?

      • A. Write the data back to the on-premises SQL Server instance.
      • B. Filter the data as the data is being read by using the Import Data module.
      • C. Run a Transact-SQL query and use SQL views to filter the data as the data is being read.
      • D. Access the on-premises SQL Server instance without using credentials, and then import the data.

      Answer: D

      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.
      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 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.
      Start of repeated scenario
      You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
      You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
      The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
      End of repeated scenario.
      You need to preprocess the training data by using a Principal Component Analysis (PCA) algorithm in the least amount of time possible. Which implementation method should you use?

      • A. Azure HDInsight using HiveML
      • B. Azure Machine Learning Studio and a custom Execute Python Script module
      • C. Azure HDInsight using Microsoft R Server
      • D. Azure Machine Learning Studio with a custom Execute R Script module

      Answer: C

      NEW QUESTION 17
      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 change a column name to a friendly name. The solution must use a native module. Which module should you use?

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

      Answer: D

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