How can you optimize the Continuous Integration (CI) pipeline for a large-scale data processing application in Azure Databricks, involving hundreds of notebooks and complex dependencies, to reduce build times while ensuring comprehensive testing coverage?

Real Exam

Developing a custom tool to analyze code changes and predict potential impact, focusing testing efforts on affected areas only

1.0%

Using Databricks Workspace API to selectively run tests based on git commit diffs, minimizing the number of notebooks tested per build

18.4%

Implementing parallel testing strategies using Azure DevOps pipelines, dynamically allocating resources based on the dependency graph of notebooks

74.8%

Leveraging a monolithic testing approach, running all tests serially to ensure environment stability and test reliability

5.8%

Databricks Certified Data Engineer - Professional