Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A team is deploying a GenAI application to a Databricks Model Serving endpoint and wants to ensure the deployment can be rolled back quickly and that traffic is shifted safely during updates. Which TWO practices should the team follow? (Choose two.)
⚠ Common exam trap
The trap here is focusing on cost and latency knobs like scale-to-zero instead of the deployment controls, such as versioned configuration and traffic splitting, that actually enable safe rollback.
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 endpoint with multiple served entities and use traffic splitting to gradually shift traffic to the new model version.
Defining endpoints as code with Databricks Asset Bundles enables repeatable deployments and fast rollback by redeploying a prior version. Configuring multiple served entities with traffic splitting allows gradual rollout to the new version, so issues can be detected early and traffic reverted quickly, together providing safe updates and rollback.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable inference tables on the endpoint to reduce logging overhead during the update.
Why it's wrong here
Disabling inference tables removes the request and response logging that helps detect regressions during a traffic shift. This makes it harder to validate the new version and to diagnose issues, which undermines safe deployment rather than supporting it.
- ✗
Deploy the new version by deleting the existing endpoint and creating a fresh one with the same name.
Why it's wrong here
Deleting and recreating an endpoint causes downtime and loses configuration such as traffic settings and permissions. It also makes rollback slower because the previous endpoint is gone, so this approach contradicts the goals of quick rollback and safe traffic shifting.
- ✗
Set the endpoint's scale-to-zero behavior to always on to avoid cold starts during the update.
Why it's wrong here
Scale-to-zero settings affect latency and cost, not the safety of traffic shifting or rollback capability. Keeping the endpoint always on may reduce cold starts, but it does not provide a mechanism to validate a new version or revert to a previous one during deployment.
- ✓
Configure the endpoint with multiple served entities and use traffic splitting to gradually shift traffic to the new model version.
Why this is correct
Model Serving supports multiple served entities on one endpoint with configurable traffic percentages. By routing a small percentage to the new version first, teams can validate behavior and shift traffic gradually, reducing risk and allowing a quick rollback by setting traffic back to the previous version.
- ✓
Use Databricks Asset Bundles to define the endpoint configuration and deploy it through a CI/CD pipeline.
Why this is correct
Databricks Asset Bundles let you version endpoint configuration as code and deploy it through CI/CD. This makes deployments repeatable and auditable, and it enables rapid rollback by redeploying a previous bundle version, which directly supports safe updates and quick recovery.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-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-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.