Which Fabric workload is used to move and transform data?

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Multiple Choice

Which Fabric workload is used to move and transform data?

Explanation:
Moving and transforming data requires a workload that orchestrates data flows between sources and destinations. Data Factory provides pipelines that connect to various data sources, copy data, apply transformations (via built-in data flows or external compute), and load results into target systems. It also handles scheduling, retries, monitoring, and data lineage, making it ideal for ETL/ELT-style tasks. The other options describe storage or analysis roles—Data Lake for raw storage, Data Warehouse for structured query-optimized storage, and Data Science for modeling and analysis—so they don’t focus on the movement and transformation aspect as Data Factory does.

Moving and transforming data requires a workload that orchestrates data flows between sources and destinations. Data Factory provides pipelines that connect to various data sources, copy data, apply transformations (via built-in data flows or external compute), and load results into target systems. It also handles scheduling, retries, monitoring, and data lineage, making it ideal for ETL/ELT-style tasks. The other options describe storage or analysis roles—Data Lake for raw storage, Data Warehouse for structured query-optimized storage, and Data Science for modeling and analysis—so they don’t focus on the movement and transformation aspect as Data Factory does.

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