Which activity should you add to Pipeline1 to copy CSV data into Lakehouse1 and support Power Query M expressions?

Which activity should you add to Pipeline1 to copy CSV data into Lakehouse1 and support Power Query M expressions?

• A. Notebook
• B. Copy data
• C. Dataflow
• D. Script

Answer: (C)

The activity you want is a dataflow. Dataflow uses Power Query M expressions for transformations, so you can define how the CSV data should be shaped and cleaned using M, then load the result into Lakehouse1. While a Copy data activity can move files directly, it doesn’t support Power Query M-based transformations. Notebooks and scripts run different runtimes (Python/Spark or other scripting) and aren’t built around Power Query M, so they wouldn’t provide the M-based transformation capability you need. Using a dataflow lets you connect to the CSV source, apply M expressions for transformations, and write the resulting data into the Lakehouse, all within the pipeline.

The activity you want is a dataflow. Dataflow uses Power Query M expressions for transformations, so you can define how the CSV data should be shaped and cleaned using M, then load the result into Lakehouse1. While a Copy data activity can move files directly, it doesn’t support Power Query M-based transformations. Notebooks and scripts run different runtimes (Python/Spark or other scripting) and aren’t built around Power Query M, so they wouldn’t provide the M-based transformation capability you need. Using a dataflow lets you connect to the CSV source, apply M expressions for transformations, and write the resulting data into the Lakehouse, all within the pipeline.