Databricks-DE-Assoc Implementing CI/CD Practice Question
A data engineering team stores all production notebooks and job definitions in a Git repository. They want every merge to the `main` branch to automatically deploy the updated notebooks to the production Databricks workspace without any manual copy/paste. Which approach should they implement?
⚠ Common exam trap
The trap here is assuming Databricks can automatically pull Git changes into a workspace without a pipeline or API call orchestrating the update.
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
✓
Configure a Databricks Repo in the production workspace that tracks the `main` branch, and run a CI/CD pipeline that calls the Repos API to update the repo to the latest commit after each merge.
Deploying from Git requires the production workspace to consume the repository as the source of truth. A Databricks Repo that tracks the `main` branch, updated by a CI/CD pipeline through the Repos API after each merge, achieves automated, traceable deployment of notebooks and job definitions without manual copying.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule a Databricks Job that executes `git pull` inside a notebook on a fixed cron schedule.
Why it's wrong here
Notebooks do not have a persistent working directory tied to a Git checkout, and running `git pull` inside a notebook does not update a Databricks Repo. This approach also lacks commit traceability and cannot reliably deploy job definitions, so it fails the automation requirement.
- ✗
Enable automatic Git synchronization on the production workspace so Databricks pulls changes from the repository on its own.
Why it's wrong here
Databricks does not offer a workspace-level automatic Git synchronization feature that pushes repository changes into production on merge. Deployment must be triggered by a CI/CD process or Repos API call, so this option describes a capability that does not exist.
- ✗
Use the Databricks CLI to export notebooks from the development workspace and import them into production on every merge.
Why it's wrong here
Exporting from the development workspace bypasses the Git repository as the source of truth and reintroduces manual workspace-to-workspace copying. It also does not deploy job definitions from the repository and provides no commit-level audit trail, which is exactly what the team wants to avoid.
- ✓
Configure a Databricks Repo in the production workspace that tracks the `main` branch, and run a CI/CD pipeline that calls the Repos API to update the repo to the latest commit after each merge.
Why this is correct
A Repo tracking `main` plus a pipeline step that calls the Repos API to pull the latest commit gives an automated, reproducible deployment of notebooks and job definitions. This keeps the production workspace in sync with the source-controlled branch and removes manual export/import steps.
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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.