Databricks-ML-Pro ML Ops Practice Question
A team wants to deploy a scikit-learn model to a real-time REST endpoint on Databricks. They have logged the model with MLflow and registered it in Unity Catalog. Which method should they use to create the serving endpoint?
⚠ Common exam trap
A common mix-up: candidates confuse local MLflow serving commands or custom Flask apps with the managed Databricks Model Serving feature.
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 the Databricks Model Serving UI or REST API to create an endpoint and select the registered model version.
Databricks Model Serving is the managed solution for deploying registered models as REST endpoints. It handles infrastructure, scaling, and dependency management automatically. Using the UI or REST API to create an endpoint from a Unity Catalog model version is the correct and supported approach, unlike manual containerization or local 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 the Databricks Model Serving UI or REST API to create an endpoint and select the registered model version.
Why this is correct
Databricks Model Serving integrates directly with Unity Catalog and MLflow. You create an endpoint via the Serving UI or the REST API, specifying the full model name and version. The service automatically provisions the necessary infrastructure and builds a container with the model's dependencies, making this the standard and supported deployment path.
- ✗
Package the model into a Docker image and deploy it to a Kubernetes cluster managed by Databricks.
Why it's wrong here
Databricks Model Serving is a fully managed service; it does not require or support user-managed Kubernetes clusters for endpoint deployment. While you can deploy models to external Kubernetes, that bypasses Databricks' integrated serving and monitoring features, adding unnecessary operational overhead.
- ✗
Write a Databricks job that loads the model and exposes it via a Flask app on a driver node.
Why it's wrong here
Running a Flask app on a job cluster driver is not a supported production serving pattern. It lacks scalability, high availability, and the security controls of Model Serving. The driver node is not designed to handle external REST traffic, and the endpoint would not survive cluster termination.
- ✗
Use MLflow's `mlflow models serve` command on a Databricks cluster to start a local REST server.
Why it's wrong here
The `mlflow models serve` command starts a local development server, typically on a single machine. It is not integrated with Databricks' production serving infrastructure, does not scale, and is not exposed as a managed endpoint. It is suitable only for local testing, not for a production real-time endpoint.
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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.