Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You use Microsoft Fabric and have linked your workspace to an Azure DevOps Git repository. A team of data engineers needs an offline development approach that lets them store the full table, view, and stored-procedure definitions for a Fabric warehouse in source control, validate schema changes during pull requests, and publish those changes through a CI/CD pipeline. They understand that, as of today, publishing to a Fabric warehouse from a DACPAC performs a full database re-deployment rather than an incremental, state-based update.
Which approach should you recommend?
Create lakehouse shortcuts that reference the warehouse delta tables and synchronize them through Git integration.
Create a SQL database project that targets Microsoft Fabric - Data Warehouse and use build and publish actions in your Azure DevOps pipeline.
Add the .pbip Power BI project for the warehouse to the repo and rely on workspace deployment pipelines to move changes.
Store all CREATE and ALTER scripts for the warehouse in a folder inside the repo and execute them in sequence from an Azure DevOps pipeline.
A SQL database project (.sqlproj) created in Azure Data Studio lets engineers import the current Fabric warehouse schema into a DACPAC-based project, manage the objects as individual script files in Git, and run build/verification as part of CI. Although SQLPackage currently performs a full re-deployment when publishing to a Fabric warehouse, database projects still provide automated drift detection, project builds, and a single source of truth for the schema. Plain T-SQL scripts lack these automated capabilities, whereas Power BI files and lakehouse shortcuts do not capture warehouse objects in a database-centric format.
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What is a SQL database project in Azure Data Studio?
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What is the role of a DACPAC in schema management for a Fabric warehouse?
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Why is using plain T-SQL scripts less effective compared to database projects?
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Microsoft Fabric Data Engineer Associate DP-700
Implement and manage an analytics solution
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