nerdexam
Microsoft

DP-100 · Question #196

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. This is the correct solution among the three variants. It properly uses Dataset.Tabular.from_delimited_files() with a glob path pattern (e.g., (datastore, 'data/*/.csv')) that captures all five quarterly CSV files across the 2018 and 2019 subfolders. It correctly registers the…

Design and prepare a machine learning solution

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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files: /data/2018/Q1.csv /data/2018/Q2.csv /data/2018/Q3.csv /data/2018/Q4.csv /data/2019/Q1.csv All files store data in the following format: id,f1,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1 You run the following code: You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code: Solution: Run the following code: Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(65 responses)
  • A
    83% (54)
  • B
    17% (11)

Explanation

This is the correct solution among the three variants. It properly uses Dataset.Tabular.from_delimited_files() with a glob path pattern (e.g., (datastore, 'data/**/*.csv')) that captures all five quarterly CSV files across the 2018 and 2019 subfolders. It correctly registers the resulting TabularDataset in the workspace under the required name using .register(workspace=ws, name='training_...'). This approach respects the file format (comma-delimited with a header row) and produces a dataset that can be consumed by training scripts. All requirements are met, so the answer is Yes.

Topics

#Azure Machine Learning Datasets#Datastores#Data Loading#Wildcards

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