Which Fabric pipeline activity supports Power Query M for copying CSV data?

DP-600 By islever Editorial Last updated

Get data Transform data

Short answer

A Add a Dataflow activity to Pipeline1, because the Dataflow activity runs Power Query M whereas Copy data does not.

Question

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. In Workspace1, you create a data pipeline named Pipeline1. You have CSV files stored in an Azure Storage account. You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1. The activity must support Power Query M formula language expressions. Which type of activity should you add?
  1. Dataflow Correct Answer
  2. Notebook
  3. Script
  4. Copy data

Key Takeaways

The question tests which Fabric pipeline activity natively runs Power Query M, and the trap is assuming the literally named "Copy data" activity is automatically correct for a copy scenario.

A Fabric pipeline in Workspace1 must copy CSV files from Azure Storage into Lakehouse1 while supporting Power Query M formula expressions. The answer confirms that a Dataflow activity meets that Power Query M requirement, whereas the Copy data activity does not.

Common mistake: The most common wrong answer is Copy data, because the task says "copy data" and Copy data activity is the obvious ingest tool — but it performs mapping-based movement and does not evaluate Power Query M expressions.

Expert Analysis

Why the Answer Is Correct

A is correct because a Dataflow (Dataflow Gen2) is the Fabric pipeline activity whose transformations are authored in Power Query, which means it natively supports Power Query M formula language expressions. Adding a Dataflow activity to Pipeline1 lets you point the query at the CSV files in the Azure Storage account, apply M-based transformation logic, and land the result in Lakehouse1. As werka noted, "You can add dataflow to pipeline as activity", and that activity is exactly where M expressions are supported. MYPE summarized the deciding fact simply: "Dataflow is Power Query." The question's constraint — "must support Power Query M formula language expressions" — is therefore satisfied only by the Dataflow activity.

Why the Other Options Are Wrong

B Notebook runs Spark code (PySpark, Scala, Spark SQL), not Power Query M, so a notebook in Pipeline1 cannot evaluate M expressions even though it can read CSV files and write to a lakehouse. C Script is a generic code-execution step rather than a Power Query M host, so it likewise fails the stated requirement. D Copy data is tempting because this is superficially a copy task, and Estratech argued that "you should add a copy activity to Pipeline1"; however, the Copy data activity handles connector-to-connector movement with column mappings and does not execute Power Query M formulas. As vernillen put it, "Although you're just copying data, which would result in a 'Copy Data' activity", the take away is that the activity "must support Power Query M".

Community Comment Notes

Nefirs admitted some ambiguity, saying "I think Dataflow since dataflow is like Power Query", and noted that a dataflow feels more like a whole component than a singular activity — a fair observation, since the Dataflow Gen2 pipeline activity is still the designated Pipeline1 step. MYPE and 2dc6125 converged on the same logic, with 2dc6125 stating Dataflow uses Power Query and doubting M support in Copy data. Estratech's reply pointing to the Lakehouse copy-activity connector documentation reflects the copy-oriented reading of the scenario, but that connector page describes the Copy data activity that has no Power Query M engine, so it does not override the M requirement in the question.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

Scan the requirement clause for the deciding keyword before looking at option names; here "Power Query M" eliminates every Spark- or copy-based activity in one step. In Fabric questions, remember that Dataflow activity equals Power Query, while Copy data equals connector-level movement with mappings only.

Frequently Asked Questions

Why is the Copy data activity wrong if the task is to copy CSV files into a lakehouse?

Copy data moves files with connector mappings but has no Power Query M engine, so it fails the stated requirement that the activity support M expressions.

Can a Dataflow activity in a Fabric pipeline actually write into a lakehouse?

Yes. Dataflow Gen2 supports a Lakehouse destination, so the same activity that evaluates your M transformations can load the CSV data into Lakehouse1.

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Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 8 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 8

  1. Nefirs 👍 12 Answered A
    I think Dataflow since dataflow is like Power Query. But question is a bit unclear. Because data flow in itself is more like a whole, well, dataflow but not a singular activity like e.g. Copy Data.
  2. Estratech 👍 3
    The answer is Correct! To copy data from CSV files to Lakehouse1 in Workspace1, you should add a copy activity to Pipeline1. https://learn.microsoft.com/en-us/fabric/data-factory/connector-lakehouse-copy-activity
  3. vernillen 👍 2 Answered A
    Although you're just copying data, which would result in a 'Copy Data' activity... you would preferably go with a Dataflow in Fabric. Especially because the big take away is that the activity must support Power Query M
  4. werka 👍 2 Answered A
    You can add dataflow to pipeline as activity: https://learn.microsoft.com/en-us/fabric/data-factory/tutorial-dataflows-gen2-pipeline-activity
  5. 2dc6125 👍 2
    A. Dataflow use powerquery. i don't think you have powerquery with copy data
Show 3 more comments
  1. 282b85d 👍 1
    Power Query M Support: Dataflows in Azure Data Factory and Synapse Analytics support the Power Query M formula language, enabling you to perform complex transformations and data manipulations as part of the data ingestion process. Transformations: Dataflows allow for a wide range of data transformation capabilities which are especially useful when working with CSV files to cleanse, aggregate, or reshape data before loading it into the destination.
  2. emmanuelkech 👍 1 Answered D
    You can specify transformations using Power Query M expressions during the data copy process. This ensures that your data is transformed according to your requirements before being loaded into Lakehouse1
  3. MYPE 👍 1 Answered A
    Dataflow is Power Query. As we need to support M formulas from Power Query, dataflow is the answer

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