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Microsoft Updated DP-100 Exam Questions and Answers by corey

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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: 422 Q&A's Shared By: corey
Question 36

You create an Azure Machine Learning workspace. You use the Azure Machine Learning Python SDK v2 to create a compute cluster.

The compute cluster must run a training script. Costs associated with running the training script must be minimized.

You need to complete the Python script to create the compute cluster.

How should you complete the script? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Questions 36

Options:

Discussion
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Question 37

You create an Azure Machine Learning workspace.

You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.

How should you complete the code segment? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Questions 37

Options:

Discussion
Question 38

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 use Azure Machine Learning designer to load the following datasets into an experiment:

Questions 38

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Execute Python Script module.

Does the solution meet the goal?

Options:

A.

Yes

B.

No

Discussion
Question 39

You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements:

• accounts for the performance of all previous runs when evaluating the current run

• avoids comparing the current run with only the best performing run to date

Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Options:

A.

Bandit

B.

Median stopping

C.

Default

D.

Truncation selection

Discussion
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