Databricks-ML-Pro ML Ops Practice Question
Your team uses Databricks Model Serving to host a production model. You need to ensure zero-downtime updates while maintaining the ability to revert to the previous version instantly if performance degrades. Which deployment strategy should you implement?
⚠ Common exam trap
Candidates often confuse 'Blue-Green' with 'A/B testing'. While they share traffic splitting, Blue-Green specifically focuses on seamless cutovers and instant rollbacks for deployment reliability, not just statistical comparison.
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
✓
Use a Blue-Green deployment strategy with traffic splitting.
Blue-green deployment allows you to maintain a production endpoint while simultaneously deploying a new version. By using weighted traffic splitting, you can shift traffic gradually and monitor performance metrics. If the new version exhibits anomalies, you can instantly revert traffic to the stable version, ensuring high availability and minimizing the impact of potential production failures, which is critical for robust MLOps workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Directly update the existing model serving endpoint to the new version.
Why it's wrong here
Direct updates create immediate downtime during the transition and lack a built-in safety mechanism for rapid rollback. In a production environment, this approach increases the risk of service disruption if the new model version encounters unexpected errors upon deployment.
- ✓
Use a Blue-Green deployment strategy with traffic splitting.
Why this is correct
Blue-Green deployment enables side-by-side versions, allowing for controlled traffic shifting. This methodology provides a seamless transition path and an instantaneous rollback capability, which are essential requirements for maintaining service reliability and managing risk in high-stakes production machine learning environments.
- ✗
Delete the current serving endpoint and create a new one with the updated model.
Why it's wrong here
Deleting an endpoint causes significant service outages. This approach is inefficient because it requires full infrastructure re-provisioning, leading to unnecessary latency and potential failures during the redeployment phase, which contradicts best practices for maintaining high-availability production systems.
- ✗
Increase the instance count of the existing endpoint before updating.
Why it's wrong here
Scaling out the cluster does not address the deployment logic or versioning safety. Without a structured traffic management strategy, the update process remains risky, and this approach does not provide the necessary isolation required for testing new models against production traffic.
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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-Pro 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-Pro exam.