Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A data science team has registered a new version of a retrieval-augmented generation (RAG) agent in Unity Catalog as a model named main.genai.support_agent. They need to make this version available as a low-latency REST endpoint with automatic scaling and no cluster management. Which Databricks capability should they use?
⚠ Common exam trap
The trap here is assuming any Databricks compute (clusters, SQL warehouses, or jobs) can serve a production REST inference endpoint, when only Mosaic AI Model Serving provides that managed contract.
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
✓
Mosaic AI Model Serving, by creating a serving endpoint that serves the registered Unity Catalog model.
Deploying a registered Unity Catalog agent model as a Mosaic AI Model Serving endpoint gives the team a managed REST inference API with automatic scaling and no cluster administration. It preserves Unity Catalog governance and lineage, supports low-latency online serving, and is the intended deployment path for GenAI agents on Databricks. Batch, interactive, and SQL compute cannot provide the same managed serving contract.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks SQL warehouses, by registering the agent as a SQL function and querying it from a dashboard.
Why it's wrong here
SQL warehouses execute SQL workloads and BI queries, not arbitrary Python agent code that depends on LangChain-style chains and external retrievers. Registering an agent as a SQL function does not produce the generic REST inference endpoint the team requires, and warehouse scaling is tuned for query concurrency rather than generative model throughput.
- ✗
All-purpose interactive clusters, by starting a cluster and calling the model through a notebook.
Why it's wrong here
All-purpose clusters are interactive development compute, not production REST endpoints. They must be started and sized manually, do not provide managed autoscaling for inference traffic, and expose no governed REST endpoint for external applications. This fails the requirement of low latency, automatic scaling, and no cluster management for a deployed agent.
- ✗
Jobs compute, by scheduling the agent as a nightly batch task and writing predictions to a Delta table.
Why it's wrong here
Jobs compute runs scheduled or triggered batch workloads, so it cannot answer interactive requests with low latency. Polling a Delta table introduces unacceptable delay for a chat or agent experience and provides no managed HTTP endpoint. This approach also requires manual cluster lifecycle handling, contradicting the no-cluster-management requirement.
- ✓
Mosaic AI Model Serving, by creating a serving endpoint that serves the registered Unity Catalog model.
Why this is correct
Mosaic AI Model Serving hosts registered Unity Catalog models and agent models behind a REST endpoint with managed, automatic scaling and no cluster management, which is exactly what the team needs for low-latency inference. Creating a serving endpoint that references the registered model version deploys it directly, preserving governance and lineage from Unity Catalog.
About these practice questions
One of 330 original Databricks-GenAI-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.