Ensuring cross-version compatibility for Databricks Notebooks is crucial with the release of new Databricks Runtime versions. What strategy best guarantees that existing notebooks remain compatible and performant across different runtime versions?

Real Exam

Manually updating a test environment to new runtime versions as they are released, running a set of benchmark notebooks, and documenting any issues

1.9%

Implementing continuous integration workflows that automatically test notebook compatibility with new runtime versions using Azure DevOps pipelines

29.2%

Setting up parallel environments in Azure Databricks, each running a different runtime version, and executing all notebooks to compare outputs and performance

16.0%

Utilizing Databricks Jobs API to programmatically run notebooks against multiple runtime versions, analyzing logs for errors or performance degradation

52.8%

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Ensuring cross-version compatibility for Databricks Notebooks is crucial with the release of new Databricks Runtime versions. What strategy best guarantees that existing notebooks remain compatible and performant across different runtime versions?