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Microsoft

DP-100 · Question #357

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. No, the Execute Python Script module does not meet the goal as the intended solution here. While you could theoretically write custom pandas code inside the Execute Python Script module to concatenate two datasets, this module is designed for custom data transformations - not…

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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 use Azure Machine Learning designer to load the following datasets into an experiment: Dataset1 Dataset2 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

  • AYes
  • BNo

How the community answered

(46 responses)
  • A
    20% (9)
  • B
    80% (37)

Explanation

No, the Execute Python Script module does not meet the goal as the intended solution here. While you could theoretically write custom pandas code inside the Execute Python Script module to concatenate two datasets, this module is designed for custom data transformations - not for the straightforward task of appending rows. The Add Rows module is the purpose-built, declarative solution in Azure ML designer for combining rows from two identically structured datasets. Using Execute Python Script would require manual coding and is not the correct designer-native approach for this task.

Topics

#Azure Machine Learning designer#Data preparation#Dataset concatenation#Module selection

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