Microsoft Fabric Data Engineer Associate DP-700 Practice Question
CSV files land in Azure Blob Storage, exposed in OneLake via a shortcut. You must build a Fabric solution that automatically starts when each new file arrives, passes the file path to an existing PySpark notebook that cleans the data, and loads the output into a Fabric warehouse. The solution must include event triggers, parameterized notebook input, and built-in retry and alerting. Which Fabric component should you create?
Create a SQL pipeline in the Fabric warehouse to run the PySpark logic and load the data.
Create a Dataflow Gen2 to transform the data and load it into the warehouse.
Create a Data Factory pipeline that contains an Execute Notebook activity followed by a Copy Data activity.
Create a Spark notebook and schedule it to run on a frequent interval.
A Data Factory pipeline in Microsoft Fabric can attach a storage event trigger, pass the triggering file path as a parameter to an Execute Notebook activity, and follow it with a Copy Data activity to load the warehouse. Pipelines expose configurable retries and monitoring/alert options without additional custom code. Stand-alone Spark notebooks lack event subscriptions, SQL pipelines cannot run PySpark, and Dataflow Gen2 centers on Power Query rather than notebook orchestration.
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Microsoft Fabric Data Engineer Associate DP-700
Implement and manage an analytics solution
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