Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A GenAI engineer has registered a fine-tuned Llama 3 model in Unity Catalog as `prod.ml.models.support_llm` and now needs to expose it as a REST endpoint for a customer-facing chatbot. The team wants Databricks to manage the serving infrastructure, GPU autoscaling, and version upgrades with minimal operational overhead. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming any Databricks compute that can load the model can also serve it as a production REST endpoint.
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 Mosaic AI Model Serving endpoint that serves the Unity Catalog model version.
Serving a Unity Catalog-registered model through Mosaic AI Model Serving is the standard Databricks pattern for production GenAI endpoints. It provides managed GPU compute, autoscaling, and versioned deployments without the engineer building infrastructure. The other choices either use batch or interactive compute that lacks an inference endpoint, or confuse storage with serving, so none can deliver a low-latency REST API for the chatbot.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Publish the model artifact to a Delta table and query it with a SQL warehouse.
Why it's wrong here
Delta tables store structured data, not model weights, and a SQL warehouse executes SQL queries rather than transformer inference. While you could store predictions in Delta, there is no mechanism to run the Llama 3 forward pass through SQL. This option confuses the data storage layer with the model serving layer and cannot deliver an interactive chatbot endpoint.
- ✗
Deploy the model to an all-purpose cluster and expose it through a notebook's REST API calls.
Why it's wrong here
All-purpose clusters are interactive compute for notebooks and jobs, not managed inference infrastructure. There is no built-in REST endpoint, autoscaling for inference traffic, or versioned rollout, so the engineer would have to hand-build a web server, load balancing, and scaling. That directly contradicts the requirement to minimize operational overhead and does not produce a production-grade serving endpoint for the chatbot.
- ✗
Register the model in the workspace model registry and call it from a Databricks job schedule.
Why it's wrong here
A scheduled job is batch-oriented: it runs on a cron trigger, not on demand per user request. A chatbot requires low-latency, request-driven inference, which a job cannot provide. Additionally, the legacy workspace model registry is superseded by Unity Catalog for governance, and neither registry by itself exposes an HTTP endpoint. This approach fails the interactive latency requirement.
- ✓
Create a Mosaic AI Model Serving endpoint that serves the Unity Catalog model version.
Why this is correct
Mosaic AI Model Serving is the managed path for exposing Unity Catalog-registered models as REST endpoints. It handles GPU provisioning, autoscaling, and workload isolation, and can serve a specific registered model version with automatic scale-to-zero and rolling upgrades. Because the model already lives in Unity Catalog, the engineer only needs to create the endpoint and point it at the model version, satisfying the low-overhead requirement.
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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.