You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

Last Updated on October 21, 2021 by Admin

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.

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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:

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 095

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 095

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:

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 096

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 096

Solution: Run the following code:

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 097

DP-100 Designing and Implementing a Data Science Solution on Azure Part 06 Q02 097

Does the solution meet the goal?

  •  Yes
  • No
Explanation:

Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn’t cleansed.

Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.

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