Databricks-ML-Pro Model Deployment Practice Question
A machine learning engineer has a model registered in Unity Catalog as prod.ml.iris_model. They need to deploy it to a real-time serving endpoint that automatically scales based on traffic and provides a REST API for predictions. The model's signature is logged. Which deployment method should they use?
⚠ Common exam trap
The trap here is assuming that any method that exposes a REST API, such as MLflow serve or a custom Flask app, is equivalent to a managed serving 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
✓
Use Databricks Model Serving to create a new endpoint and select the model from Unity Catalog, specifying the model version or alias.
Databricks Model Serving provides a fully managed, scalable solution for deploying models registered in Unity Catalog. It automatically creates a REST endpoint, handles scaling, and integrates with governance features. The other options either rely on manual infrastructure or are intended for development, not production real-time serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use MLflow's built-in serving command to start a local REST server on a cluster and expose it via a public URL.
Why it's wrong here
MLflow serving is designed for local testing or development, not for production-grade, auto-scaling endpoints. It does not integrate with Unity Catalog for model selection and lacks the managed scalability, security, and monitoring features of Databricks Model Serving. Exposing a cluster's local server publicly is not a supported production pattern.
- ✓
Use Databricks Model Serving to create a new endpoint and select the model from Unity Catalog, specifying the model version or alias.
Why this is correct
Databricks Model Serving is the managed service for real-time inference. It integrates with Unity Catalog, allowing you to select a registered model by name and version or alias. The endpoint provides a REST API, auto-scales based on load, and handles the serving infrastructure, which matches the requirement for a scalable real-time endpoint.
- ✗
Create a Databricks job that runs a Python script to load the model and listen for HTTP requests on a driver node.
Why it's wrong here
Running a custom HTTP server on a driver node is not a managed serving solution. It would require manual setup for scaling, security, and high availability, and it would not automatically integrate with Unity Catalog model permissions or provide the built-in monitoring and governance of Databricks Model Serving.
- ✗
Export the model as a Docker image using MLflow and deploy it to a Kubernetes cluster managed outside Databricks.
Why it's wrong here
While exporting models to Docker is possible, it requires external infrastructure and does not leverage Databricks Model Serving's native integration with Unity Catalog, auto-scaling, or unified governance. The scenario asks for a deployment method within Databricks that provides a REST API and auto-scaling, which is directly offered by Model Serving.
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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-ML-Pro 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-ML-Pro exam.