Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer has registered a model in the Databricks Model Registry. The model must be deployed to a REST endpoint that automatically scales with traffic and provides a stable serving environment. Which Databricks capability should they use?
⚠ Common exam trap
The trap here is thinking that any Python web server on a cluster constitutes model serving, when only Databricks Model Serving provides managed autoscaling and a stable 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
✓
Databricks Model Serving with the registered model version as the served entity.
Databricks Model Serving provides a managed, serverless endpoint for registered model versions. It automatically scales with request volume and offers a stable serving environment, eliminating the need to run and maintain a web server. The other options either require manual server management or are intended for interactive or batch use, not production REST serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the model as an MLflow artifact and load it in a Databricks notebook using mlflow.pyfunc.load_model for interactive scoring.
Why it's wrong here
Loading a model in a notebook is suitable for interactive scoring or batch inference, not for a production REST endpoint that scales with traffic. Notebooks are not designed to be always-on services, and they lack the autoscaling and stable endpoint characteristics of a managed serving solution.
- ✓
Databricks Model Serving with the registered model version as the served entity.
Why this is correct
Databricks Model Serving is a fully managed, serverless solution that deploys registered model versions as REST endpoints with automatic scaling and a stable serving environment. It handles infrastructure, scaling, and monitoring, so the engineer only needs to select the model version and enable serving, meeting the deployment requirement without managing servers.
- ✗
MLflow Model Registry webhooks that trigger a deployment script when the model version transitions to Production.
Why it's wrong here
Webhooks can notify external systems of registry events, but they do not host the model or provide a REST endpoint. They merely trigger a script, which would still need to deploy and manage a server. This does not deliver automatic scaling or a stable serving environment on its own.
- ✗
A Databricks job that runs a Python web server on a cluster and exposes the model via a public URL.
Why it's wrong here
Running a custom web server on a cluster is not a managed serving solution. The cluster does not automatically scale with traffic, the endpoint is not stable across cluster restarts, and the engineer must manage availability, security, and scaling. This approach is operationally heavy and does not provide the automatic scaling or stable environment required.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.