Implement Lifecycle Management in Fabric DP-700 Practice Questions (Page 7)

Questions 31–35

1. 31expert · hard

   A Fabric deployment pipeline has three stages: Development, Test, and Production. The Test stage uses a separate capacity than Development and Production. When promoting a dataset from Development to Test, the deployment fails because the dataset references a warehouse that does not exist in the Test workspace. You need to ensure future deployments succeed without manual intervention. What should you do?

   • APromote the warehouse separately before the dataset.
   • BRemove the warehouse reference from the dataset before deploying.
   • CUse deployment rules to map the warehouse reference to the Test workspace's warehouse.
   • DCreate the missing warehouse in the Test workspace and then redeploy.
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2. 32foundation · easy

   Which Fabric item is used to create and manage a database project?

   • ASQL analytics endpoint
   • BData Factory pipeline
   • CDatabase project
   • DWarehouse
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4. 33foundation · easy

   What is the primary purpose of a deployment pipeline in Microsoft Fabric?

   • ATo manage user permissions
   • BTo move items between environments such as development, test, and production
   • CTo create backups of data in lakehouses
   • DTo monitor query performance
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5. 34application · medium

   A Fabric deployment pipeline has three stages. You need to ensure that when a dataset is promoted to Production, it uses a specific data source that is different from the one in Development. What should you configure?

   • AA deployment rule that overrides the data source for the Production stage.
   • BA Power Automate flow that changes the data source after deployment.
   • CA separate dataset in Production that is manually updated.
   • DA Git branch for Production with different dataset definitions.
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6. 35expert · hard

   A Fabric team uses a 'main' branch for production and 'develop' for integration. They want to use GitFlow-like branching where features are developed in separate branches and merged into 'develop' via pull requests. They also want to ensure that the 'main' branch only receives changes that have been tested and approved. Which configuration best supports this?

   • AEnable branch policies on 'main' and 'develop' to require pull request reviews and successful builds before merging.
   • BUse a single branch for all work and rely on deployment pipeline approvals.
   • CUse a deployment pipeline to deploy from 'develop' to production directly.
   • DAllow developers to push directly to 'main' after local testing.
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