Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer has an existing Databricks Model Serving endpoint named churn-endpoint serving version 3 of a model. The team has validated version 5 and wants to direct live traffic to it while keeping the deployment reversible if quality degrades. What is the most appropriate action?
⚠ Common exam trap
The trap here is believing that a Model Registry stage transition automatically changes which model version a serving endpoint uses.
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
✓
Update the served entity in the endpoint configuration to reference model version 5, allowing rollback by re-pointing to version 3
The endpoint serves whichever model version URI its configuration references, so updating the served entity to version 5 redirects live traffic through a managed rollout and keeps rollback simple by re-pointing to version 3. Deleting and recreating, batch overwrites, and registry stage changes do not achieve reversible live traffic redirection.
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 the endpoint and create a new one with a different name that serves model version 5
Why it's wrong here
Deleting the endpoint breaks existing consumers that call the original URL and eliminates a clean rollback path, since the previous endpoint no longer exists. Recreating under a new name also forces clients to change their configuration. This approach is disruptive and fails the reversibility requirement.
- ✓
Update the served entity in the endpoint configuration to reference model version 5, allowing rollback by re-pointing to version 3
Why this is correct
Updating the served entity to the new model version URI performs a managed rolling deployment while preserving the ability to revert by re-pointing to the earlier version. This keeps the change reversible and uses the endpoint's native configuration, matching both the traffic redirection and rollback requirements without extra infrastructure.
- ✗
Transition model version 5 to Production in the Model Registry and assume the endpoint automatically follows the stage change
Why it's wrong here
Endpoints reference a specific model version URI, not a stage. Changing the registry stage does not automatically re-point the endpoint, so live traffic would continue to hit version 3. This misconception leaves the deployment unchanged despite the registry update, failing the requirement to serve version 5.
- ✗
Keep the endpoint on version 3 and write a scheduled job that calls version 5 and overwrites predictions in the serving table
Why it's wrong here
A scheduled job does not redirect live endpoint traffic; clients would still receive version 3 predictions. Overwriting stored predictions also creates inconsistency and cannot serve real-time requests. This fails to move production inference to version 5 while adding unnecessary complexity.
About these practice questions
This Databricks-ML-Assoc question is part of Courseiva's 319-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.