Databricks-ML-Pro Model Deployment Practice Question
A data scientist has registered a model in Unity Catalog and wants to deploy it to a Databricks Model Serving endpoint. What is the simplest way to create the endpoint?
⚠ Common exam trap
A common mix-up: candidates confuse model deployment methods, such as local serving or batch jobs, with the managed Model Serving endpoint creation process.
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 UI to create a new serving endpoint and select the registered model.
The Databricks UI offers a simple, integrated way to create a Model Serving endpoint by selecting a registered Unity Catalog model. It handles configuration and deployment automatically, making it the easiest method for data scientists.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Databricks job that runs the model's predict method on a schedule.
Why it's wrong here
A scheduled job performs batch inference, not real-time serving. It does not expose an endpoint for on-demand requests. This approach is for batch processing and does not fulfill the requirement of deploying to a Model Serving endpoint.
- ✗
Write a Python script using the MLflow library to deploy the model to a local server.
Why it's wrong here
MLflow can serve models locally, but this does not create a Databricks Model Serving endpoint. It requires manual infrastructure and is not integrated with Databricks' managed serving. This approach is unsuitable for production deployment on Databricks.
- ✓
Use the Databricks UI to create a new serving endpoint and select the registered model.
Why this is correct
The Databricks UI provides a straightforward, guided workflow to create a serving endpoint by selecting a registered model from Unity Catalog. It automatically configures the endpoint with the model's environment and dependencies, making it the simplest method for deployment.
- ✗
Export the model as a pickle file and upload it to a Databricks cluster.
Why it's wrong here
Exporting a pickle file and uploading it to a cluster does not create a serving endpoint. It lacks the managed serving infrastructure, scaling, and API endpoint provided by Databricks Model Serving. This method is manual and error-prone.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-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-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.