A data engineer needs to develop integration tests for an ETL process and deploy a...

Databricks Asset Bundles provide a modern, declarative approach to defining, testing, and deploying Databricks workflows as version-controlled code artifacts . Using YAML configuration files, engineers can describe jobs, pipelines, dependencies, and environments in a structured and reproducible way. Asset Bundles integrate well with CI/CD pipelines and external schedulers, enabling teams to package workflows, run integration tests, and deploy consistently across environments such as development, staging, and production. This aligns directly with the requirement for version control and external orchestration. The Databricks CLI (A) is primarily used as a deployment interface but does not provide the structured packaging and workflow definition capabilities of Asset Bundles. Databricks Connect (C) is intended for local development and testing of Spark code, not workflow deployment. The Databricks SDK (D) provides programmatic access to APIs but lacks the declarative, packaged deployment model required here. Databricks documentation positions Asset Bundles as the recommended solution for production-grade workflow deployment and testing within modern data engineering practices.