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2018 May New Microsoft 70-774 Real Exam Dumps with PDF and VCE Free Updated Today! Following are some new 70-774 Real Exam Questions:

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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

Answer: C
Explanation: warehouse-style-queries-on-hyper-scale-data-2/

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: B

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

Answer: D

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

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

Answer: B

You are building an Azure Machine Learning experiment.
You need to transform 47 numeric columns into a set of 10 linearly uncorrelated features.
Which module should you add to the experiment?

A. Principal Component Analysis
B. K-Means Clustering
C. Normalize Data
D. Group Data into Bins

Answer: A

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


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