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Answer: Utilizing Databricks Repos to directly integrate your notebooks with Git, enabling version control and linking to Azure DevOps for CI pipelines
1. **Utilizing Databricks Repos**: This feature allows for direct version control of notebooks within the Databricks workspace, enabling change tracking, version reversion, and team collaboration. 2. **Integrating with Git**: Linking Databricks Repos with Git enhances version control, leveraging Git's robust features for code change management. 3. **Linking to Azure DevOps for CI pipelines**: This setup automates the testing and integration of notebook changes, ensuring they are deployment-ready and error-free early in the development cycle. Together, these steps establish a comprehensive version control and CI process for Databricks notebooks, facilitating efficient collaboration and confident deployment.
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Your team is using Azure Databricks notebooks for development and aims to implement version control and continuous integration (CI) practices. Which approach best integrates these practices into your workflow?
A
Exporting notebooks as Python files for version control purposes, but without any continuous integration process
B
Ignoring version control for notebooks due to their interactive nature and focusing only on CI for data pipeline infrastructure code
C
Utilizing Databricks Repos to directly integrate your notebooks with Git, enabling version control and linking to Azure DevOps for CI pipelines
D
Manually copying notebooks into a version-controlled repository and reviewing changes before each deployment