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Microsoft Updated DP-100 Exam Questions and Answers by paris

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Microsoft DP-100 Exam Overview :

Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Code: DP-100 Dumps
Vendor: Microsoft Certification: Microsoft Azure
Questions: 506 Q&A's Shared By: paris
Question 48

You need to select a pre built development environment for a series of data science experiments. You must use the R language for the experiments.

Which three environments can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options:

A.

MI.NET Library on a local environment

B.

Azure Machine Learning Studio

C.

Data Science Virtual Machine (OSVM)

D.

Azure Data bricks

E.

Azure Cognitive Services

Discussion
Question 49

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

Minimum nodes: 2

Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

Minimum nodes: 0

Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Options:

A.

Azure Machine Learning designer

B.

Azure CLI ml extension v2

C.

Azure Machine Learning studio

D.

BuildContext class in Python SDK v2

E.

MLCIient class in Python SDK v2

Discussion
Question 50

You are profiling mltabte data assets by using Azure Machine Learning studio. You need to detect columns with odd or missing values. Which statistic should you analyze?

Options:

A.

Profile

B.

Std deviation

C.

Error count

D.

Type

Discussion
Question 51

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:

A.

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

B.

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

C.

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

D.

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

E.

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
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