Which Fabric pipeline activity supports Power Query M for copying CSV data?
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?
Community Votes
100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
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.
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.
Community Discussion (8 comments)
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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
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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