Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist has registered a model in the Databricks Model Registry and wants to deploy it as a REST API endpoint for real-time inference. Which Databricks feature should they use?
⚠ Common exam trap
A common mix-up: candidates confuse model tracking or batch scoring with real-time serving; MLflow Tracking records experiments but does not serve models.
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
✓
MLflow Model Serving
For real-time inference of registered models, Databricks offers MLflow Model Serving, which creates a managed REST endpoint. It integrates with the Model Registry, allowing you to serve specific model versions with automatic scaling. Other options are for analytics, tracking, or batch processing, not for serving models as APIs.
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 used to schedule and run notebooks or JARs for batch processing, not for real-time serving. A job could be used to batch-score data, but it does not provide a persistent REST endpoint for low-latency inference. It is unsuitable for real-time API requirements.
- ✗
MLflow Tracking
Why it's wrong here
MLflow Tracking is used to record and query experiments, including parameters, metrics, and artifacts. It does not provide a serving endpoint for models. While it stores model artifacts, it does not expose them as a REST API. Using it for deployment would require additional infrastructure and custom code.
- ✗
Databricks SQL Analytics
Why it's wrong here
Databricks SQL Analytics is designed for querying and visualizing data using SQL, not for serving machine learning models. It can be used to analyze predictions if they are stored in tables, but it does not provide a real-time inference endpoint. It lacks the model serving capabilities required for low-latency predictions.
- ✓
MLflow Model Serving
Why this is correct
MLflow Model Serving in Databricks provides a fully managed, scalable endpoint for real-time inference of models registered in the Model Registry. It handles deployment, scaling, and monitoring, and exposes a REST API. This directly matches the requirement to serve a registered model as a REST endpoint without managing infrastructure.
About these practice questions
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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-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.