Databricks-ML-Assoc ML Workflows Practice Question
A data scientist has trained a model and wants to deploy it for real-time inference with automatic scaling and without managing infrastructure. Which Databricks feature should they use?
⚠ Common exam trap
Many exam-takers confuse the Model Registry, which stores model versions, with Model Serving, which actually hosts the model for predictions.
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 managed solution for deploying MLflow models as scalable REST endpoints. It handles provisioning, scaling, and monitoring, so the data scientist can focus on the model. It integrates with the Model Registry and supports real-time inference without infrastructure overhead.
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 SQL endpoint
Why it's wrong here
Databricks SQL endpoints are optimized for SQL analytics and BI workloads, not for serving machine learning models. They cannot load MLflow models or provide REST endpoints for predictions. Using a SQL endpoint for model inference is not a supported pattern and would not provide the required serving capabilities.
- ✓
Databricks Model Serving
Why this is correct
Databricks Model Serving provides a fully managed, serverless endpoint for real-time inference. It automatically scales based on traffic and requires no infrastructure management. You can enable it directly from the Model Registry or via the serving UI, making it the correct choice for this scenario.
- ✗
A standalone Flask app on a Databricks cluster
Why it's wrong here
Running a Flask app on a cluster requires manual setup, does not automatically scale, and is not managed by Databricks. It would need custom infrastructure and monitoring, which contradicts the requirement to avoid infrastructure management. Databricks Model Serving is the managed alternative designed for this purpose.
- ✗
MLflow Model Registry
Why it's wrong here
The MLflow Model Registry is a centralized store for managing model versions and their lifecycle stages. It does not serve predictions or provide endpoints. While it integrates with serving, it is not the deployment mechanism itself, so it does not meet the requirement of real-time inference with automatic scaling.
About these practice questions
This Databricks-ML-Assoc question is part of Courseiva's 319-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-Assoc 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-Assoc exam.