Loading CSV Data Assets into Pandas with MLTable
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 during interactive development. You need to access and load the folder data asset into a Pandas data frame. Which method should you use to achieve this goal?
Community Votes
80% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests knowledge of MLTable source builders; the trap is confusing file formats (CSV vs. Parquet) or assuming a generic load method handles specific formatting without explicit configuration.
To load a CSV file from a folder data asset into a Pandas DataFrame using Azure Machine Learning, you must use the mltable.from_delimited_files() method. Community consensus confirms that CSV is a type of delimited file, making option B the correct choice over parquet or generic loading methods.
Candidates often select C (mltable.from_data_lake()) because they recognize the 'folder' concept, but this method is for accessing Azure Data Lake Storage paths directly rather than building an MLTable object from local or registered assets for wrangling. Others might pick D if they misunderstand the initialization process.
Community Discussion (6 comments)
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Expert Analysis
Why the Answer Is Correct
The correct answer is B,mltable.from_delimited_files(). In Azure Machine Learning, CSV files are classified as delimited files. The mltable library provides specific builder methods to create MLTable objects from different data sources. Since the input is a CSV file within a registered folder data asset, from_delimited_files() is the appropriate method to define the schema and load the data.Why the Other Options Are Wrong
Option A (from_parquet_files()) is incorrect because it is designed specifically for Parquet format, not CSV. Option C (from_data_lake()) is typically used to reference paths in Azure Data Lake Storage Gen2, not for creating an MLTable instance from a registered workspace data asset for interactive wrangling in this context. Option D (load()) is not the standard method for constructing an MLTable from raw files; it is generally used to load existing MLTable definitions.Community Comment Notes
Comments [1], [3], [4], and [5] all correctly identify B as the answer. Comment [3] explicitly links the logic: "csv=>delimited file," which is the key conceptual step. Comment [2] initially voted for C but provided a link to the official documentation forfrom_delimited_files, inadvertently supporting the correct answer despite their vote.