Microsoft passcert dp-100 Vce Exam Download by Greyson q239 vce pdf

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Exam Name:	Designing and Implementing a Data Science Solution on Azure
Exam Code:	DP-100 Dumps
Vendor:	Microsoft	Certification:	Microsoft Azure
Questions:	422 Q&A's	Shared By:	greyson

Question 32

An organization uses Azure Machine Learning service and wants to expand their use of machine learning.

You have the following compute environments. The organization does not want to create another compute environment.

Questions 32

You need to determine which compute environment to use for the following scenarios.

Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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.

Questions 32

Options:

Discussion

Question 33

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment

You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.

You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

What should you do?

Options:

Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

Always run the experiment with an Estimator by using the default packages.

Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

Create a config. yaml file defining the conda packages that are required and save the file in the experiment folder.

Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

Discussion

Question 34

Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

support Python and Scala
compose data storage, movement, and processing services into automated data pipelines
the same tool should be used for the orchestration of both data engineering and data science
support workload isolation and interactive workloads
enable scaling across a cluster of machines

You need to create the environment.

What should you do?

Options:

Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

Discussion

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Question 35

: 213 HOTSPOT

You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.

You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

You run the following code.

Questions 35