Databricks-DE-Assoc Implementing CI/CD Practice Question
A data engineering team wants to implement Git integration for their Databricks notebooks. Which workflow is considered the best practice for CI/CD in Databricks Repos?
⚠ Common exam trap
Candidates often suggest manual notebook exports or direct Git commits to production branches, ignoring the necessity of pull requests for code review and automated deployment workflows.
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
✓
Develop code in feature branches, merge via pull requests, and use Databricks Repos to sync production.
Integrating Databricks Repos with a Git provider like GitHub enables version control at the notebook level. This workflow allows teams to use feature branches for development, submit pull requests for code review, and merge into a main branch that triggers automated deployments. Adopting this standardizes the development lifecycle, ensures code traceability, and prevents manual, error-prone deployments in production environments, which is essential for maintaining production-grade data pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export notebooks manually to local machines and commit them via Git CLI.
Why it's wrong here
Manual exporting of notebooks creates a significant maintenance burden and increases the risk of version drift. This approach bypasses the built-in Git integration in Databricks, making it difficult to maintain audit trails and collaborative workflows. Automation through Databricks Repos is the preferred standard for modern CI/CD pipelines.
- ✗
Use the Databricks REST API to push code updates directly to production notebooks.
Why it's wrong here
Pushing code updates directly to production notebooks via API skips the crucial testing and validation phases of a CI/CD pipeline. This practice eliminates the possibility of peer reviews and automated testing, significantly increasing the likelihood of deploying breaking changes into production environments without adequate oversight or rollback capabilities.
- ✓
Develop code in feature branches, merge via pull requests, and use Databricks Repos to sync production.
Why this is correct
This workflow leverages standard DevOps practices like branching and pull requests to ensure code quality through peer reviews. By using Databricks Repos to sync, the production environment stays consistent with the verified main branch, minimizing configuration errors and ensuring that only tested, approved code is executed in production workflows.
- ✗
Develop code in the production workspace and use Git only for final snapshots.
Why it's wrong here
Developing directly in a production workspace violates basic security and operational best practices. It makes the production environment unstable and prevents proper isolation between development, testing, and production stages. Git should be used throughout the development lifecycle, not just as a tool for taking occasional snapshots of the environment.
About these practice questions
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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.