Databricks-ML-Pro Model Deployment Practice Question
A data scientist has trained a model and registered it in Unity Catalog. They now need to deploy it for real-time inference with automatic scaling and a REST API endpoint. Which Databricks feature should they use?
⚠ Common exam trap
Many candidates confuse model registry event triggers with actual serving infrastructure, or assuming that batch jobs can handle real-time requests.
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
✓
Databricks Model Serving
Databricks Model Serving is the purpose-built feature for deploying models as real-time REST endpoints with automatic scaling. It handles infrastructure, scaling, and monitoring, allowing data scientists to focus on model development. Other options are either for automation, batch processing, or data governance, and do not provide the required real-time serving capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog functions
Why it's wrong here
Unity Catalog functions allow you to define and share logic as SQL or Python functions, but they are not intended for deploying machine learning models for real-time inference. They are used for data transformations and governance, not for serving model predictions with automatic scaling or REST endpoints.
- ✓
Databricks Model Serving
Why this is correct
Databricks Model Serving provides a fully managed, serverless solution to deploy models as REST API endpoints with automatic scaling based on traffic. It integrates with Unity Catalog for model governance and supports real-time inference. This is the standard feature for deploying models for real-time serving in Databricks, offering built-in monitoring and scaling without managing infrastructure.
- ✗
Databricks Jobs with a serving cluster
Why it's wrong here
Databricks Jobs are designed for batch or scheduled workloads, not for real-time, low-latency inference. While you can run a job that starts a prediction server, it does not provide automatic scaling or a managed REST API. Jobs are not suitable for serving models that require immediate responses to individual requests.
- ✗
MLflow Model Registry webhooks
Why it's wrong here
MLflow Model Registry webhooks are used to trigger actions when model events occur, such as when a model version transitions to a new stage. They do not provide an inference endpoint or automatic scaling. Webhooks are for automation and notification, not for serving predictions.
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.