Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer needs to deploy a model for real-time inference with automatic scaling and a REST endpoint that requires token-based authentication. The model artifacts are already registered in the Databricks Model Registry. Which Databricks capability should be used?
⚠ Common exam trap
The trap here is treating a continuous job or MLflow's local serve command as production serving, when only Model Serving provides managed autoscaling and token-secured endpoints.
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, which creates a scalable REST endpoint for registered model versions with built-in authentication and autoscaling.
Databricks Model Serving provides managed, autoscaling REST endpoints for registered model versions, with authentication handled through Databricks tokens or service principals. It is the native capability for real-time inference and requires no custom Flask hosting, cluster networking, or manual scaling logic, unlike the other options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MLflow's built-in model serving command, which deploys the model to a local REST server on the cluster and exposes it through the workspace URL.
Why it's wrong here
The mlflow models serve command starts a local server for testing, not a managed, scalable endpoint. It does not integrate with Databricks authentication, autoscaling, or workspace URL routing, so it cannot provide a production-grade, token-secured REST endpoint for real-time inference.
- ✗
A Databricks Job with a continuous trigger that runs a notebook hosting a Flask application on the driver node.
Why it's wrong here
A continuous job runs on cluster nodes that are not designed for low-latency external traffic, and the driver is not exposed as a public endpoint. Authentication and autoscaling for HTTP requests are not provided. This approach would require significant custom networking and security work, making it unsuitable for real-time serving.
- ✓
Databricks Model Serving, which creates a scalable REST endpoint for registered model versions with built-in authentication and autoscaling.
Why this is correct
Databricks Model Serving is designed to expose registered model versions as REST endpoints, handling autoscaling, availability, and authentication through Databricks tokens or service principals. It integrates directly with the Model Registry, so the engineer can enable serving on a version and get a secured endpoint without managing infrastructure.
- ✗
Databricks SQL endpoint with an AI function that calls the registered model for each row in a query.
Why it's wrong here
Databricks SQL AI functions are oriented toward batch or interactive SQL queries, not low-latency real-time REST inference with autoscaling. They also do not expose a token-authenticated HTTP endpoint for arbitrary clients. This option addresses a different access pattern than the real-time serving requirement.
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.