DP-100 · Question #378
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset. You plan to use the folder data asset for data wrangling…
The correct answer is B. mltable.from_delimited_files(). To access and load a registered folder data asset containing a CSV file into a Pandas DataFrame for interactive development, a specific method from the mltable library is required.
Question
Options
- Amltable.from_parquet_files()
- Bmltable.from_delimited_files()
- Cmltable.from_data_lake()
- Dmltable.load()
How the community answered
(27 responses)- A7% (2)
- B70% (19)
- C4% (1)
- D19% (5)
Why each option
To access and load a registered folder data asset containing a CSV file into a Pandas DataFrame for interactive development, a specific method from the `mltable` library is required.
The `mltable.from_delimited_files()` method is designed to create an MLTable from delimited text files, such as CSVs, which are located within a specified path. This MLTable can then be easily converted into a Pandas DataFrame for data wrangling tasks.
`mltable.load()` is used to load an already defined MLTable object from an MLTable YAML definition file, not to create one directly from raw source files like CSVs in a folder.
Concept tested: Loading delimited data assets into MLTable for Pandas
Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?tabs=cli#create-mltable-data-assets
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