Loading CSV Data Assets into Pandas with MLTable

DP-100 By islever Editorial Last updated

Short answer

B mltable.from_delimited_files()

Question

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?
  1. mltable.from_parquet_files()
  2. mltable.from_delimited_files() Correct Answer
  3. mltable.from_data_lake()
  4. mltable.load()

Key Takeaways

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.

Common mistake: 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.

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 for from_delimited_files, inadvertently supporting the correct answer despite their vote.

Community Votes

B
80%
C
20%

80% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 6 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 6

  1. 3a0b61c 👍 1 Answered C
    correct https://learn.microsoft.com/en-us/python/api/mltable/mltable?view=azure-ml-py#mltable-from-delimited-files
  2. avinyc 👍 1 Answered B
    mltable.from_delimited_files()
  3. evangelist 👍 1 Answered B
    csv=>delimited file
  4. nicorg5 👍 1
    answer is correct
  5. sai384957324 👍 1 Answered B
    B is correct
Show 1 more comment
  1. 401d299 👍 1 Answered B
    correct

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