Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer has registered a model in the Databricks Model Registry and wants to expose it as a REST API with automatic scaling and no server management. The model's Python dependencies are captured in a conda environment file logged with the run. Which Databricks capability should the engineer use to serve this model with minimal operational overhead?
⚠ Common exam trap
The trap here is assuming any Databricks compute can host a model endpoint, when only Model Serving provides managed REST inference for registered model versions.
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 a model version URI from the Model Registry
Databricks Model Serving is the managed capability that takes a registered model version URI, reconstructs the environment from logged dependencies, and publishes a scalable REST endpoint without server administration. The other choices describe batch, interactive, or SQL compute that cannot deliver managed low-latency REST inference for a registered MLflow model.
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 Databricks job that runs the model on a schedule and writes predictions to a Delta table
Why it's wrong here
A scheduled job performs batch scoring, not real-time REST inference. It cannot answer synchronous HTTP requests with low latency, and it introduces scheduling delay. The scenario requires an interactive API endpoint, which a recurring job cannot provide, so this approach fails to meet the serving requirement.
- ✓
Databricks Model Serving with a model version URI from the Model Registry
Why this is correct
Databricks Model Serving directly consumes a registered model version URI, builds the serving environment from the logged conda dependencies, and exposes a REST endpoint with managed scaling. It requires no cluster or server administration, matching the requirement for minimal operational overhead while providing low-latency inference for the registered model.
- ✗
A SQL warehouse configured with the model's conda environment installed on all nodes
Why it's wrong here
SQL warehouses execute SQL workloads and do not host arbitrary Python MLflow models as REST endpoints. They cannot load the model's conda environment for Python inference, so they cannot serve the model. Using a SQL warehouse here misapplies the compute type and does not fulfill the REST API requirement.
- ✗
An all-purpose cluster running an MLflow model server process started manually
Why it's wrong here
An all-purpose cluster is interactive and not designed for production REST serving; it lacks managed autoscaling for inference traffic and requires manual process supervision. Starting an MLflow model server on it does not provide the managed endpoint, high availability, or scaling behavior that Databricks Model Serving offers for a registered model version.
About these practice questions
Courseiva writes every Databricks-ML-Assoc question from scratch — 319 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.