Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your team must transform daily CSV files that land in a lakehouse's Bronze layer. The transformation calls a custom Python function that requires installing a third-party statistics package and must run on a scalable Spark cluster that you can trigger from a Fabric pipeline on a daily schedule. Which Microsoft Fabric component should you use to implement the transformation logic?
Build a Dataflow Gen2 that performs the transformation with Power Query steps.
Create a notebook in the Data Engineering experience and write the transformation in PySpark.
Implement the logic in a T-SQL stored procedure inside a Fabric warehouse.
Author a KQL query set that embeds Python code to process the data.
A Fabric notebook created in the Data Engineering experience lets you write PySpark code, use %pip or the library management UI to install additional Python packages, and runs on an automatically provisioned Spark cluster that scales to the data volume. You can invoke the notebook from a Fabric pipeline activity to execute it on a schedule. Although KQL query sets support embedded Python, they execute inside Kusto and don't allow custom pip installs or leverage Spark for large-scale batch transformations. Dataflows Gen2 rely on Power Query transformations and don't expose a Spark runtime for custom PySpark code. T-SQL stored procedures run on the Relational Data Warehouse engine and can't import external Python packages.
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What is PySpark and why is it used in the Data Engineering experience?
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How does a Fabric pipeline trigger a notebook on a schedule?
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Why can't KQL or T-SQL handle the custom transformations in this scenario?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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