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Installing a Maven package on every node requires changing Cluster1's library configuration. The CAN MANAGE permission grants the cluster-level management capabilities required to add or remove libraries without giving User1 broader control over the entire workspace or Azure resource group. CAN RESTART allows a user to restart the cluster but does not provide sufficient authority to change its library configuration. Assigning the Azure Contributor role would grant excessive control over the resource group and does not represent the appropriate Databricks object-level permission. Making User1 a workspace administrator would provide substantially more privileges than necessary. Granting CAN MANAGE directly on Cluster1 therefore satisfies the operational requirement while limiting User1's authority to the specific compute resource that must be modified.

The Databricks CLI contains the bundle command group for validating, deploying, and running Declarative Automation Bundles. CLI commands can be executed consistently from developer terminals or CI/CD pipelines, making deployments automated, repeatable, and suitable for multiple target environments. The Databricks SDK for Python can manage workspace APIs programmatically, but it is not the standard bundle deployment interface required here. Git folders provide source-control integration inside the workspace but do not deploy bundle-defined resources. The Jobs UI supports interactive creation and management of jobs, which introduces manual steps and does not provide the same infrastructure-as-code workflow. Using commands such as databricks bundle validate and databricks bundle deploy -t dev directly satisfies the automated development deployment requirement. Microsoft Learn