Implementing CI/CD

A data engineering team uses Databricks Asset Bundles to deploy jobs and notebooks. When they run 'databricks bundle deploy -t dev', the engineer wants to confirm where the bundle's files and resources are placed so multiple developers on the same team do not overwrite each other's deployments in the 'dev' target. By default, how does a Databricks Asset Bundle isolate a developer's deployment in the 'dev' target?

A data engineering team's Databricks Asset Bundle project has grown large, with dozens of job and pipeline resource definitions all placed directly inside the top-level databricks.yml file. The lead engineer wants to split these resource definitions into separate YAML files under a resources/ directory so each team member can maintain their own jobs without causing merge conflicts in one giant file. Which databricks.yml mechanism allows these separate resource files to be combined into the bundle configuration?

A data engineer has defined a Databricks Asset Bundle containing a job resource in databricks.yml. After committing changes to Git, they want to push the updated job definition and its associated files to the Databricks workspace for the 'dev' target, but they do NOT want to trigger execution of the job yet. Which Databricks CLI command accomplishes this?

A data engineering team stores their Databricks Asset Bundle project in a Git repository. A new engineer clones the repo and needs to understand which file defines the bundle's name, the resources (jobs and pipelines) to deploy, and the available deployment targets. Which file in the project contains this core configuration?

A data engineering team manages a Databricks Asset Bundle that deploys a job to both a staging and a production workspace. When they promote a new release, they want the production job to always run the code from a specific, immutable Git tag rather than from files copied into the production workspace. Which approach best supports this requirement?

A data engineering team wants every new Databricks Asset Bundle project to start with a consistent folder structure, a pre-configured databricks.yml, and standard job definitions that match company conventions. They want engineers to scaffold new projects quickly without copying files manually. Which Databricks CLI capability should they use to accomplish this?

A data engineering team uses Databricks Asset Bundles. Each developer deploys the same bundle to a shared workspace from their own feature branch. They report that when two developers deploy simultaneously, their jobs collide — one developer's deployment overwrites the other's job and schedules start firing unexpectedly during testing. They want each developer's deployment to be isolated (uniquely named per user) and to have schedules automatically paused. Which configuration in the target should they use?

A data engineering team uses Databricks Asset Bundles to deploy jobs. During development, jobs run under each developer's personal identity, which is fine for testing. However, when deploying to the production target, the team wants all jobs to run under a shared service principal rather than the identity of whoever last ran `databricks bundle deploy`. Which configuration approach in the bundle's databricks.yml achieves this for the production target?

A data engineering team uses Databricks Asset Bundles (DABs) to deploy the same job to a development workspace and a production workspace. In development, jobs should run on a small single-node cluster and write to a dev catalog; in production, jobs should run on a larger cluster and write to a prod catalog. The team wants a single bundle definition that adapts these settings based on where it is deployed. What is the recommended way to achieve this?

A data engineer has finished editing the databricks.yml configuration and several job definitions for a Databricks Asset Bundle. Before deploying to the development target, they want to confirm that the bundle configuration is syntactically correct and that all references resolve properly, without actually creating or updating any resources in the workspace. Which CLI command should they run?

A data engineering team maintains a Databricks Asset Bundle whose databricks.yml defines several jobs. Each job needs to reference the same notification email address and the same Unity Catalog catalog name in multiple places. The team wants to define these values in ONE location and reuse them throughout the bundle configuration, so a single edit updates every reference. Which Asset Bundle feature should they use?

A data engineering team uses Databricks Repos linked to a remote GitHub repository. One engineer edits a notebook inside their personal Repo in the workspace and wants their changes to be reviewed by teammates through the team's normal pull request process before merging to the main branch. What should the engineer do from the Databricks Repos Git interface?

A data engineering team uses Databricks Repos to manage their notebooks under Git version control. A new engineer clones the remote repository into their own Databricks Repo folder, makes changes to a notebook on a feature branch, and wants their teammates to be able to review the changes before they are merged into the main branch. Which workflow correctly accomplishes this using Databricks Repos and Git?

A data engineering team uses Databricks Repos linked to a remote GitHub repository. Two engineers are working on separate feature branches derived from 'main'. Engineer A merges their pull request into 'main' first. When Engineer B later attempts to merge their pull request, GitHub reports a merge conflict on the same notebook. What is the recommended way for Engineer B to resolve this within a proper Git workflow before merging?