Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data science team is using Databricks Repos to manage a machine learning project. They want to ensure that their notebooks and supporting modules are version-controlled and that they can collaborate without overwriting each other's changes. Which TWO practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is assuming that workspace revision history or DBFS provides the branching and merge capabilities needed for collaborative version control.
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
✓
Clone the remote Git repository into Databricks Repos and commit changes from the Repos UI or a terminal.
To collaborate effectively with Databricks Repos, teams should clone the remote Git repository into Repos and use Git branches for feature work, merging changes through pull requests. These practices provide isolation, version control, and a review process, ensuring that collaborators do not overwrite each other's work.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store notebooks only in the Databricks workspace and rely on workspace revision history for version control.
Why it's wrong here
Workspace revision history provides a limited audit trail but lacks branching, merging, and remote collaboration features. It does not support the Git-based workflows needed for parallel development, and changes can still be overwritten if multiple users edit the same notebook concurrently.
- ✓
Clone the remote Git repository into Databricks Repos and commit changes from the Repos UI or a terminal.
Why this is correct
Cloning the repository into Repos links the workspace to the remote Git repository. Users can commit and push changes from the Repos UI or a terminal, keeping the remote repository as the source of truth. This enables proper version control and collaboration, including conflict resolution.
- ✗
Enable automatic Git commits on every notebook save to avoid manual versioning steps.
Why it's wrong here
Databricks Repos does not automatically commit on every save. Committing is a deliberate action that allows users to group related changes and write meaningful messages. Automatic commits would create noise and could commit incomplete work, undermining collaboration and code quality.
- ✓
Use Git branches within Databricks Repos to isolate feature work and merge changes through pull requests.
Why this is correct
Databricks Repos integrates with Git, allowing users to create and switch branches directly in the workspace. Using branches for feature work and merging via pull requests provides isolation and review, preventing team members from overwriting each other's changes and maintaining a clear version history.
- ✗
Use DBFS to store notebooks and manually copy files to a Git repository outside Databricks.
Why it's wrong here
DBFS is a distributed file system for data, not a version control system. Manually copying files to Git is error-prone, lacks atomic commits, and does not provide branching or merge capabilities. This approach would not prevent overwrites and would complicate collaboration.
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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-ML-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-ML-Assoc exam.