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Microsoft

DP-100 · Question #212

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…

The correct answer is B. No. The answer is No because the proposed solution does not correctly configure the run to make the training data file available on the remote compute cluster. When running a script on a remote compute target like 'aml-compute', all files needed by the script - including the…

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Question

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 a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder. You must run the script as an Azure ML experiment on a compute cluster named aml-compute. You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster. Solution: Run the following code: Does the solution meet the goal?

Exhibit

DP-100 question #212 exhibit

Options

  • AYes
  • BNo

How the community answered

(37 responses)
  • A
    46% (17)
  • B
    54% (20)

Explanation

The answer is No because the proposed solution does not correctly configure the run to make the training data file available on the remote compute cluster. When running a script on a remote compute target like 'aml-compute', all files needed by the script - including the training data - must either be included in the source directory uploaded to the cluster or referenced via a registered Azure ML datastore/dataset. The solution likely omits the training data from the source directory or fails to pass it as a dataset input, so the script would fail to find 'data.csv' on the remote compute.

Topics

#Azure Machine Learning#Command Jobs#Environments#Data Handling

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