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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 mltable.from_delimited_files() is specifically designed to load delimited files (like CSV) into MLTable objects, which can then be converted to Pandas DataFrames. The community discussion shows strong consensus for option B (67% of votes), with multiple users confirming it's correct for CSV files. Option D (mltable.load()) is mentioned in one comment for loading from Azure ML data assets, but it's less specific for CSV files and has lower community support. Options A and C are incorrect: A is for Parquet files, and C (from_data_lake) 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 plan to use this 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()