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Microsoft

DP-100 · Question #194

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 - the solution does not correctly create the required dataset. The datastore contains CSV files spread across subdirectories (/data/2018/ and /data/2019/). To register a TabularDataset that covers all these files, the correct approach uses…

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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 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?

Exhibit

DP-100 question #194 exhibit

Options

  • AYes
  • BNo

How the community answered

(22 responses)
  • A
    23% (5)
  • B
    77% (17)

Explanation

The answer is No - the solution does not correctly create the required dataset. The datastore contains CSV files spread across subdirectories (/data/2018/ and /data/2019/). To register a TabularDataset that covers all these files, the correct approach uses Dataset.Tabular.from_delimited_files() with a wildcard path pattern such as (datastore, '/data/**/*.csv'). The failing solution likely uses an incorrect path (e.g., pointing to only one subdirectory, or using a FileDataset instead of a TabularDataset), which means not all quarterly files are included, or the dataset is not registered with the correct name. Proper registration also requires calling .register(workspace, name='training_data', ...) after defining the dataset.

Topics

#Azure Machine Learning#Datastore#Dataset Creation#Data Loading

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