Which statement correctly describes using a Data Factory workload to create a Dataflow Gen2 for transformation before loading into a lakehouse?

• A. Connect to Data Factory workload > Create a Dataflow Gen2 to transform data > Add your lakehouse as the data destination.
• B. Connect to Data Factory workload > Create Pipeline to copy data > Load to lakehouse directly.
• C. Connect to Data Factory workload > Create a Dataflow Gen2 to transform data > export to a separate dataset.
• D. Connect to Real-time intelligence workload > Create a Dataflow Gen2 to transform data > Add your lakehouse as the data destination.

Answer: (A)

Transforming data inside a Data Flow Gen2 in Data Factory before loading into a lakehouse mirrors the ETL pattern used for lakehouse architectures. Data Flow Gen2 provides a visual, scalable way to apply cleansing, joins, aggregations, and other transformations within a pipeline. You set up a Data Flow Gen2, map the source, apply the transformations, and choose the lakehouse as the sink so the output lands there after processing. This is exactly what the described workflow does: connect to Data Factory, create a Dataflow Gen2 to transform data, and designate the lakehouse as the destination.

The other approaches don’t fit because they bypass transformation or use a setup not aligned with this pattern: copying data directly into the lakehouse without transformation, exporting to a separate dataset rather than loading into the lakehouse, or invoking a Real-time intelligence workload, which isn’t the Data Factory workflow described for batch transformation and loading.