Databricks-DE-Assoc Implementing CI/CD Practice Question
A data engineering team is implementing a CI/CD pipeline for Databricks notebooks and jobs using Databricks Asset Bundles. They want to ensure deployments are reproducible and that production changes are traceable. Which TWO of the following practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that syncing manual production edits back to Git later is acceptable, when any manual edit breaks the link between the deployed state and a specific commit.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a separate target in databricks.yml for each environment, and run databricks bundle deploy with the appropriate target in the pipeline.
Reproducible and traceable deployments require that the bundle and notebooks are versioned together and deployed from a specific commit. Using environment-specific targets in databricks.yml ensures consistent deployment across workspaces. Manual edits, embedded credentials, and local deployments all break reproducibility and traceability, so they must be avoided.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the bundle from a developer's local machine to production to avoid the complexity of a CI/CD runner.
Why it's wrong here
Deploying from a local machine is not reproducible, auditable, or consistent. It depends on the developer's environment and bypasses automated tests and approvals. A CI/CD runner is necessary to enforce the pipeline stages, capture logs, and ensure that only committed code is deployed to production.
- ✓
Use a separate target in databricks.yml for each environment, and run databricks bundle deploy with the appropriate target in the pipeline.
Why this is correct
Targets allow a single bundle to be deployed to different workspaces with environment-specific settings, such as workspace host and variable values. Invoking the deploy command with the correct target in each pipeline stage ensures consistent, repeatable deployments. This is the recommended way to manage multiple environments with Databricks Asset Bundles.
- ✗
Manually edit the deployed job in the production workspace after deployment to apply urgent fixes, and sync those changes back to Git later.
Why it's wrong here
Manual edits in the production workspace create drift between the deployed state and the Git repository. This makes deployments non-reproducible and breaks traceability, because the running job no longer matches any commit. Urgent fixes should go through the pipeline via a hotfix branch and pull request, not direct workspace edits.
- ✗
Embed production credentials directly in the databricks.yml file so the pipeline can authenticate without additional configuration.
Why it's wrong here
Embedding credentials in databricks.yml exposes secrets in version control, which is a serious security risk. Authentication should be handled through secure mechanisms like service principals with OAuth tokens or federated identity, stored outside the repository. This practice would compromise the pipeline and is never recommended.
- ✓
Store the databricks.yml bundle configuration and all referenced notebooks in the same Git repository, and deploy from a specific Git commit SHA.
Why this is correct
Keeping the bundle configuration and notebooks together in version control ensures that a deployment is defined by a single commit. Deploying from a specific commit SHA makes the deployment reproducible and traceable, because you can identify exactly which code revision is running in production. This is a core CI/CD practice for Databricks Asset Bundles.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Assoc exam.