Databricks-ML-Pro ML Ops Practice Question
Your organization needs to automate the deployment of models to real-time serving endpoints. Which service within the Databricks ecosystem handles the hosting and scaling of these endpoints with managed containerization?
⚠ Common exam trap
Candidates often confuse Databricks Model Serving with MLflow tracking or model registry features, picking general tracking tools instead of the dedicated endpoint hosting service designed for real-time containerized serving.
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 provides a fully managed, scalable infrastructure for deploying models as REST APIs. It handles the underlying container orchestration, allowing developers to focus on the model artifact rather than infrastructure management. This is essential for MLOps, as it simplifies the transition from a registered model in the MLflow registry to a production-ready, highly available endpoint that handles real-time traffic efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Jobs
Why it's wrong here
Databricks Jobs are designed for running scheduled or triggered batches of notebooks, JARs, or Python scripts. While they can perform batch inference, they are not intended for real-time model serving. They lack the low-latency API infrastructure required for real-time, request-response machine learning workloads.
- ✓
Databricks Model Serving
Why this is correct
Model Serving is the dedicated service for hosting models. It manages the server infrastructure, auto-scaling, and deployment of models from the registry. This allows data science teams to deploy models via a single click or API call, ensuring consistent, scalable production performance without manual configuration of servers.
- ✗
Delta Live Tables
Why it's wrong here
Delta Live Tables is a declarative framework for building reliable data pipelines. It is focused on ETL, data cleaning, and data transformation. It is not an inference engine and cannot host models to serve real-time predictions to external applications or services.
- ✗
Databricks Repos
Why it's wrong here
Databricks Repos provides Git integration for versioning notebooks and files. While it is a critical tool for MLOps, it is a development/source-control tool, not a hosting/serving infrastructure. It plays no role in the runtime execution or serving of model artifacts to end-user applications.
About these practice questions
Courseiva writes every Databricks-ML-Pro question from scratch — 300 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.