nerdexam
Microsoft

DP-100 · Question #373

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 A. Yes. To combine all rows from two datasets while maintaining their original columns and headers in Azure Machine Learning designer, the solution suggests using the Join Data module.

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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 Join Data module. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(43 responses)
  • A
    93% (40)
  • B
    7% (3)

Why each option

To combine all rows from two datasets while maintaining their original columns and headers in Azure Machine Learning designer, the solution suggests using the Join Data module.

AYesCorrect

While the 'Add Rows' module is typically used for vertical concatenation (appending rows), a Full Outer Join in the 'Join Data' module can be configured to include all non-matching rows from both input datasets. If the input datasets have identical schemas and a full outer join is performed without a common key, or using a dummy key, the resulting dataset can effectively contain all rows and columns, thereby meeting the goal of creating a unified dataset with all original rows.

BNo

The 'Join Data' module is primarily designed for horizontally merging datasets based on key columns, which typically combines columns and not just appends rows, making 'Add Rows' the more direct module for the stated goal.

Concept tested: Dataset concatenation in Azure ML designer

Source: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/join-data

Topics

#Azure Machine Learning Designer#Data Transformation#Dataset Concatenation#Data Preparation

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