Databricks-ML-Assoc Databricks Machine Learning Practice Question
When a data scientist needs to share an MLflow experiment with a team member, what is the best practice for ensuring collaborative access within Databricks?
⚠ Common exam trap
Candidates often assume that sharing a notebook automatically grants access to the associated experiments. They fail to realize that experiment permissions are managed separately within the MLflow UI.
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
✓
Granting appropriate permissions on the experiment within the MLflow UI.
Sharing experiments is done by managing permissions on the experiment object itself. In Databricks, you can grant specific permissions (e.g., CAN_VIEW, CAN_MANAGE) to individual users or groups. This allows for controlled collaboration where team members can see experiment results without necessarily having the ability to modify or delete them, adhering to the principle of least privilege while fostering efficient teamwork during the development process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Exporting the experiment to a CSV file and emailing it.
Why it's wrong here
Emailing data is insecure and breaks the collaborative workflow provided by the Databricks platform. It creates data silos and prevents real-time collaboration. Using the native platform sharing features is essential for security and ensuring that all team members work from a single source of truth for experiment data.
- ✓
Granting appropriate permissions on the experiment within the MLflow UI.
Why this is correct
Granting permissions within the Databricks UI ensures secure and efficient collaboration. This allows team members to view or manage the experiment directly, maintaining data governance and auditability. It is the recommended practice for teams, as it centralizes access management and ensures that experiment results remain securely contained within the workspace.
- ✗
Moving the experiment to a public folder in DBFS.
Why it's wrong here
Moving assets to a public folder exposes them to all users in the workspace, which violates security best practices. Experiments should be managed via formal access control lists, not by moving them to folders. This approach lacks granular control and increases the risk of accidental modification or data leakage.
- ✗
Giving everyone admin access to the workspace.
Why it's wrong here
Granting admin access to all team members is a major security risk that contradicts the principle of least privilege. Admin rights should be strictly limited to a small number of users. Collaborative access should be granted through specific object permissions, not by elevating user roles across the entire platform environment.
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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.