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Answer: mltable.from_delimited_files()
The question asks for loading a CSV file from a registered folder data asset into a Pandas DataFrame during interactive development. CSV files are delimited files, and the mltable.from_delimited_files() method is specifically designed to load delimited files (like CSV) into an MLTable object, which can then be converted to a Pandas DataFrame. This is supported by the community consensus (67% selected B) and multiple comments confirming B is correct, with reasoning that 'csv=>delimited file'. While mltable.load() (option D) can load data assets, it is more general and typically used with Azure ML data asset URIs, not directly with file types. Options A and C are incorrect: A is for Parquet files, and C is not a valid mltable method.
Author: LeetQuiz Editorial Team
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You have an Azure Machine Learning workspace with a folder containing a CSV file. The folder is registered as a folder data asset.
You intend to use this folder data asset for data wrangling during interactive development.
You need to load the folder data asset into a Pandas DataFrame.
Which method should you use?
A
mltable.from_parquet_files()
B
mltable.from_delimited_files()
C
mltable.from_data_lake()
D
mltable.load()
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