Databricks-ML-Pro ML Ops Practice Question
A team runs a weekly retraining job that produces a new model version in Unity Catalog. Their production endpoint is currently serving version 4. They want to promote version 5 with zero downtime and the ability to roll back instantly if error rates rise. Which approach best meets these requirements?
⚠ Common exam trap
The trap here is conflating a rolling update on a single endpoint with running duplicate endpoints or deleting the prior version.
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 the serving endpoint to serve version 5 and rely on the platform's rolling update so traffic shifts gradually while the previous configuration remains available for rollback.
Model Serving supports rolling updates that keep the endpoint live while a new model version loads, and the previous configuration remains restorable. Repointing the endpoint to the newer version and letting the rolling update run achieves zero-downtime promotion with a straightforward rollback. Deleting the old version or duplicating endpoints introduces risk or cost without improving the outcome.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete version 4 from the registry, deploy version 5, and recreate the endpoint so it picks up the new version automatically.
Why it's wrong here
Deleting the currently served version removes the fallback and can break the running endpoint. Recreating the endpoint also causes an outage while the new endpoint provisions, violating the zero-downtime requirement. This approach discards the safety net instead of preserving it, so a failed promotion would leave the team with no working version to restore.
- ✓
Configure the serving endpoint to serve version 5 and rely on the platform's rolling update so traffic shifts gradually while the previous configuration remains available for rollback.
Why this is correct
Databricks Model Serving performs rolling updates that keep the endpoint available while new model versions warm up, and the prior configuration can be restored if problems appear. Pointing the endpoint at the new version and letting the rolling update proceed gives zero-downtime promotion with a fast rollback path, which matches both stated requirements without extra tooling.
- ✗
Set the endpoint's served entities to version 5 with a traffic percentage of zero, then raise traffic to one hundred percent after validation.
Why it's wrong here
Zero-percent traffic means the endpoint never actually routes requests to version 5, so validation would produce no signal and the promotion would not take effect. While traffic splitting is useful for canary testing, the described sequence as written leaves the new version unserved. The team would still need a later configuration change to make the new version live.
- ✗
Create a second endpoint for version 5, run both endpoints in parallel indefinitely, and split traffic manually at the load balancer.
Why it's wrong here
Running two endpoints doubles cost and adds operational complexity, and manual load-balancer splitting is not how Model Serving traffic is managed. It also does not provide the instant rollback the team wants, since reverting requires reconfiguring the balancer. The scenario asks for zero-downtime promotion on one endpoint, which a rolling update already delivers.
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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.