Databricks-ML-Assoc ML Workflows Practice Question
A team is transitioning from local model development to production in Databricks. Which THREE practices should be implemented to ensure a successful MLOps workflow?
⚠ Common exam trap
Candidates often focus only on model training, forgetting that production MLOps requires a holistic approach including continuous monitoring for drift and automated CI/CD deployment pipelines.
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
✓
Always register models in the MLflow Model Registry.
Moving to production requires rigorous automation and governance. Using a centralized model registry ensures version control and auditability. Automated CI/CD pipelines ensure that model training and deployment are consistent and repeatable, reducing the risk of manual errors. Finally, monitoring model performance in production is critical to detect drift and trigger retraining, ensuring the model remains accurate and relevant as the underlying data distribution changes over time in the real-world environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Always register models in the MLflow Model Registry.
Why this is correct
Registering models provides a centralized, version-controlled repository. This allows for clear tracking of which model version is in which stage, enables audit logs of changes, and facilitates integration with deployment tools, which is necessary for managing model provenance and ensuring only approved versions are deployed to production endpoints.
- ✗
Keep all training and production code in a single notebook for simplicity.
Why it's wrong here
Storing production code in a single notebook makes testing, versioning, and collaborative development difficult. Best practices dictate moving logic into modular Python files or libraries that can be unit-tested, version-controlled via Git, and imported into Databricks notebooks, which improves code quality and maintainability in a production machine learning environment.
- ✓
Implement automated CI/CD pipelines for deployment.
Why this is correct
CI/CD pipelines reduce the risk of human error during deployment. By automating testing and model promotion, you ensure that only models meeting specific quality gates reach production, which increases deployment velocity, ensures consistency across environments, and enables rapid rollbacks if performance metrics in the production environment drop unexpectedly.
- ✓
Monitor model performance and trigger retraining when drift is detected.
Why this is correct
Model drift is inevitable in real-world applications. Monitoring performance metrics in production allows you to detect when the model's predictive accuracy degrades. Automating the retraining trigger ensures that models are updated with recent data, maintaining high performance and ensuring the model remains effective over time without requiring constant manual intervention.
- ✗
Allow all team members to have 'Admin' access to production models.
Why it's wrong here
Granting excessive permissions violates the principle of least privilege. Production models should be protected by strict access controls to prevent accidental deletion or unauthorized modification. Only authorized service accounts or specific personnel should have the ability to promote models or modify production registry settings, maintaining the integrity of the system.
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-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.