Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A team is deploying a GenAI agent built with Mosaic AI Agent Framework. The agent must be exposed as a REST API that automatically scales with traffic and records inference logs to Unity Catalog. Which deployment mechanism meets these requirements with the least operational overhead?
⚠ Common exam trap
The trap here is assuming any compute that can run Python can serve an agent, when only a Model Serving endpoint provides autoscaling REST access with built-in inference logging.
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
✓
Deploy the agent to a Databricks Model Serving endpoint using the agents.deploy() API.
The agents.deploy() API is the native Mosaic AI Agent Framework mechanism for taking an agent from development to a production Model Serving endpoint. It handles endpoint creation, scaling, and integrates with Unity Catalog inference tables for logging, so it directly satisfies all stated requirements with minimal manual work.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy the agent to a Databricks Model Serving endpoint using the agents.deploy() API.
Why this is correct
The agents.deploy() API in the Mosaic AI Agent Framework is purpose-built to deploy agents to a Model Serving endpoint. It automatically provisions a scalable REST endpoint, enables inference table logging to Unity Catalog, and integrates with the agent's evaluation and monitoring stack, requiring minimal manual configuration.
- ✗
Deploy the agent on a single-node cluster with a public IP and expose it via a Flask server.
Why it's wrong here
A manually managed Flask server on a single-node cluster lacks autoscaling, high availability, and native Unity Catalog inference logging. It also introduces significant operational overhead for security, patching, and scaling, which contradicts the requirement to minimize operational effort.
- ✗
Package the agent as a Python wheel and run it on a Databricks job cluster with a scheduled trigger.
Why it's wrong here
A Databricks job cluster is designed for batch or scheduled workloads, not for serving low-latency REST API requests. It does not provide an autoscaling HTTP endpoint or automatic inference table logging, so it fails the requirement for scalable real-time serving with Unity Catalog logging.
- ✗
Register the agent as a Unity Catalog model and call it directly from a notebook using mlflow.pyfunc.load_model().
Why it's wrong here
Loading a model directly in a notebook is an interactive development pattern, not a production deployment. It provides no REST endpoint, no autoscaling, and no automatic inference logging. It also requires an active cluster, so it does not satisfy the operational requirements for a scalable API.
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.