Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist registered a model in Unity Catalog and now wants to serve it as a low-latency REST endpoint for an application. They need automatic scaling, a secure endpoint URL, and the ability to update the served model version without redeploying infrastructure. Which Databricks capability should they use?
⚠ Common exam trap
The trap here is treating scheduled batch jobs or SQL warehouses as substitutes for a managed low-latency serving endpoint.
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
✓
Model Serving with a served entity pointing at the registered model version.
Model Serving provides managed, autoscaling REST endpoints backed by registered model versions. Configuring a served entity that references a Unity Catalog model and version gives a secure URL and lets the team change the served version without rebuilding the endpoint, satisfying low-latency, secure, and updatable serving needs in one capability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A SQL warehouse with a user-defined function wrapping the model.
Why it's wrong here
SQL warehouses serve SQL workloads and can invoke models through functions, but they are not designed to provide a dedicated low-latency REST endpoint with per-model versioning and automatic scaling for online application traffic. They also lack the model-serving configuration semantics such as served entities. This approach does not meet the online inference and version update requirements.
- ✗
An all-purpose cluster running an MLflow model server manually.
Why it's wrong here
Running the MLflow model server on an all-purpose cluster is manual, does not auto-scale with traffic, and lacks the managed secure endpoint and version-switching behavior. The cluster must be maintained and restarted, and there is no integrated serving URL. This fails the requirements for automatic scaling, managed security, and seamless version updates without infrastructure redeployment.
- ✓
Model Serving with a served entity pointing at the registered model version.
Why this is correct
Databricks Model Serving creates managed REST endpoints that automatically scale, provide a secure URL, and can be configured with a served entity that references a Unity Catalog model and version. Updating the served version changes what the endpoint serves without rebuilding infrastructure, which matches the requirements for low latency, security, and version flexibility in this scenario.
- ✗
A Databricks job that runs a notebook on a schedule to score requests.
Why it's wrong here
Scheduled jobs are batch-oriented and do not expose a low-latency REST endpoint for an application to call synchronously. They also lack the automatic request-driven scaling and secure serving URL described. While jobs can perform inference, they cannot satisfy the interactive latency and always-available endpoint requirement, making this unsuitable for the application integration described.
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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.