Microsoft 70-774 Online Practice Questions and Exam Preparation

Microsoft 70-774 Online Questions & Answers

• Question 41:

  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.
  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.

• Question 42:

  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
  A. overfitting

• Question 43:

  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.
  D. Access the on-premises SQL Server instance without using credentials, and then import the data.

• Question 44:

  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.

  

  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
  A. Yes

• Question 45:

  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
  B. No

• Question 46:

  You are building an Azure Machine Learning solution for an online retailer.

  When a customer selects a product, you need to recommend products that the customer might like to purchase at the same time. The recommendation should be based on what other customers purchased when they purchased the same

  product.

  Which model should you use?

  A. Collaborative filtering
  B. Boosted Decision Tree Regression model
  C. Two-Class boosted decision tree
  D. K-Means Clustering
  A. Collaborative filtering

• Question 47:

  DRAG DROP

  You need to create a training set for use with a linear regression model, and then to test the training set.

  How should you create the training set? To answer, drag the appropriate modules to the correct locations. 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:

  

  

• Question 48:

  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. importing Python modules from a ZIP file for execution in a Machine Learning experiment
  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. returning multiple data frames
  A. importing Python modules from a ZIP file for execution in a Machine Learning experiment

• Question 49:

  You are performing exploratory analysis of files that are encoded in a complex proprietary format. The format requires disk intensive access to several dependent files in HDFS.

  You need to build an Azure Machine Learning model by using a canopy clustering algorithm. You must ensure that changes to proprietary file formats can be maintained by using the least amount of effort. Which Machine Learning library should you use?

  A. MicrosoftML
  B. scikit-learn
  C. SparkR
  D. Mahout
  C. SparkR

• Question 50:

  You are building an Azure Machine Learning experiment.

  You need to transform a string column that has 47 distinct values into a binary indicator column. The solution must use the One-vs-All Multiclass model.

  Which module should you use?

  A. Select Column Transform
  B. Convert to Indicator Values
  C. Group Categorical Values
  D. Edit Metadata
  B. Convert to Indicator Values