Microsoft 70-774 Online Practice Questions and Exam Preparation

Microsoft 70-774 Online Questions & Answers

• Question 51:

  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
  D. Spark

• Question 52:

  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
  A. Yes

• Question 53:

  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
  D. Select Columns in Dataset

• Question 54:

  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
  C. Azure Machine Learning Studio

• Question 55:

  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
  A. Yes

• Question 56:

  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
  D. Select Columns in Dataset

• Question 57:

  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
  A. a Hive user-defined function (UDF)

• Question 58:

  You have a dataset that is missing values in a column named Column3. Column3 is correlated to two columns named Column4 and Column5.

  You need to improve the accuracy of the dataset, while minimizing data loss. What should you do?

  A. Replace the missing values in Column3 by using probabilistic Principal Component Analysis (PCA).
  B. Remove all of the rows that have the missing values in Column4 and Column5.
  C. Replace the missing values in Column3 with a mean value.
  D. Remove the rows that have the missing values in Column3.
  A. Replace the missing values in Column3 by using probabilistic Principal Component Analysis (PCA).

• Question 59:

  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
  A. Execute Python Script

• Question 60:

  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.
  B. Create an Azure HDInsight Hadoop cluster.
  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.