Databricks-ML-Assoc Model Deployment Practice Question
A team has an existing Databricks Model Serving endpoint serving `prod.ml.fraud_model` version 3. They register version 4, which uses a new feature set, and want to shift only 10% of traffic to version 4 while keeping version 3 for the rest. Their endpoint currently has a single served entity for version 3. What is the most appropriate approach?
⚠ Common exam trap
The trap here is assuming that updating a served entity to a new version performs a gradual rollout, when in fact it replaces the served version entirely.
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
✓
Add a second served entity for version 4 to the same endpoint and configure traffic splitting between the two served entities.
Traffic splitting in Databricks Model Serving is achieved by defining multiple served entities on a single endpoint and assigning each a traffic percentage. Adding a served entity for version 4 at 10% and leaving version 3 at 90% implements the desired canary rollout. Swapping the served entity version, using an external load balancer, or renaming the model do not provide the controlled split.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Register version 4 under a new model name and point the existing served entity at the new model name.
Why it's wrong here
Renaming the model and repointing the served entity would move all traffic to version 4 and abandon version 3, defeating the canary goal. It also breaks the version lineage under the original model name, complicating rollback and governance.
- ✓
Add a second served entity for version 4 to the same endpoint and configure traffic splitting between the two served entities.
Why this is correct
Databricks Model Serving supports multiple served entities on one endpoint with configurable traffic percentages. Adding a served entity for version 4 and setting its traffic share to 10% achieves the canary rollout while version 3 continues to receive the remaining traffic.
- ✗
Update the existing served entity's entity_version to 4 and rely on the endpoint's automatic gradual rollout.
Why it's wrong here
Changing the served entity's version replaces version 3 entirely; Model Serving does not automatically perform a gradual rollout when a version is swapped. This would send all traffic to version 4 rather than the intended 10%.
- ✗
Create a second endpoint for version 4 and configure a load balancer outside Databricks to split traffic 10/90 between endpoints.
Why it's wrong here
While an external load balancer could split traffic, this adds unnecessary infrastructure and does not use Databricks' native traffic splitting. The scenario asks for the most appropriate approach within Databricks Model Serving, which supports multiple served entities directly.
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.