You have a Fabric lakehouse named Lakehouse1. Forecast data stored in Azure Data Lake Storage Gen2. You plan to ingest the forecast data into Lakehouse1. The data is already formatted, and you do NOT need to apply any further data transformations. The solution must minimize development effort and costs. Which method should you recommend to efficiently ingest the data?

• A. Use the Copy activity in a pipeline
• B. Use a custom Spark job
• C. Use Dataflow Gen2
• D. Do nothing; data is already in Lakehouse

Answer: (A)

The situation tests choosing a lightweight, managed data movement method when no transformations are needed. Since the forecast data is already formatted and you want to minimize development effort and cost, a simple pipeline with a Copy activity is the best fit. The Copy activity is designed to move data from a source like Azure Data Lake Storage Gen2 into a Fabric lakehouse with little to no coding, handling file-based ingestion efficiently and reliably. It can preserve the existing schema and structure, supports scheduling or automated runs, and avoids the overhead of building and maintaining custom compute jobs.

Using a custom Spark job would add coding, cluster management, and compute costs, which isn’t necessary here since no transformations are required. Dataflow Gen2 offers rich ETL capabilities, but that extra functionality isn’t needed for straightforward data movement and would introduce more setup and cost. Doing nothing would miss the ingestion step entirely.