Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
An engineer maintains a GenAI application that uses a Databricks Asset Bundle to deploy a Mosaic AI Agent serving endpoint. A new model version has been logged and validated, and the team wants to roll it out to production with the ability to revert quickly if quality regressions appear. Which deployment approach best satisfies this?
⚠ Common exam trap
The trap here is assuming a new model version requires a new endpoint, when the served entity on an existing endpoint can be updated and reverted in place.
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
✓
Create a new serving endpoint version by updating the endpoint's served model entity to the new model version, then verify quality and roll back by pointing the entity back to the previous version.
Updating the served model entity on an existing endpoint moves production traffic to the validated model version while keeping the endpoint URL and configuration stable, so clients are unaffected. If quality regresses, reverting the served entity to the previous version restores prior behavior quickly. Recreating endpoints, notebook-only testing, and cross-workspace routing each introduce downtime, delay, or unnecessary complexity instead of a clean, reversible cutover.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the existing endpoint unchanged and run the new model version only in notebooks until the team is confident, then deploy it later.
Why it's wrong here
Running the new version only in notebooks does not put it into production, so it does not deliver the requested rollout. It also provides no production traffic signal and no controlled cutover mechanism. The team asked for a production rollout with quick revert, which this approach postpones rather than achieves.
- ✓
Create a new serving endpoint version by updating the endpoint's served model entity to the new model version, then verify quality and roll back by pointing the entity back to the previous version.
Why this is correct
Updating the served entity on the existing endpoint switches traffic to the new model version while preserving the endpoint URL and configuration, enabling a fast rollback by reverting the served entity to the prior version. This gives the team a quick, low-risk path to production with a clear revert option if regressions appear.
- ✗
Deploy the new model version to a second workspace and route production traffic there through a global load balancer.
Why it's wrong here
Cross-workspace routing adds significant operational complexity, splits governance and monitoring, and is not how Mosaic AI Model Serving endpoints are versioned. Rollback would require rerouting traffic between workspaces, which is slower and riskier than reverting a served entity. This over-engineers the rollout and does not leverage endpoint-level version control.
- ✗
Delete the production endpoint and create a brand-new endpoint with the new model version, then update all clients to the new URL.
Why it's wrong here
Deleting the endpoint causes downtime and forces every client to change its URL, which is disruptive and error-prone. Rollback would require recreating the old endpoint and re-updating clients again. This approach maximizes blast radius instead of providing a safe, reversible rollout for a validated model version.
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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-GenAI-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-GenAI-Assoc exam.