Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer has a model registered in Unity Catalog and wants to expose it as a REST API so an external application can send JSON payloads and receive predictions. The team has no existing serving infrastructure. Which Databricks feature should be used to create this API?
⚠ Common exam trap
The trap here is equating any way of running a model, such as a scheduled job or SQL function, with a serving endpoint that exposes an HTTP API.
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
✓
A Databricks Model Serving endpoint created from the registered model.
Model Serving is the Databricks capability that turns a registered model into a managed REST endpoint with autoscaling and request-response semantics. Scheduled notebook jobs, experiment tracking runs and SQL warehouses serve other purposes, so they cannot provide the low-friction HTTP API the external application needs for on-demand scoring.
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 a notebook on a schedule and writes predictions to a table.
Why it's wrong here
A scheduled notebook job produces batch results written to storage; it does not expose an HTTP endpoint that an external application can call on demand. The external app would have to poll the output table and match its own requests to rows, which is not a real-time API. This is a batch pattern, not interactive serving.
- ✗
A Databricks SQL warehouse with a query that invokes the model as a user-defined function.
Why it's wrong here
A SQL warehouse executes analytical queries and exposes JDBC or ODBC connections and a query API, not a model-scoring REST endpoint for arbitrary JSON feature payloads. While SQL can call certain model functions, the interface and semantics differ from a serving endpoint, and the external app would need to construct SQL rather than send feature vectors. This is an analytics access path, not model serving.
- ✓
A Databricks Model Serving endpoint created from the registered model.
Why this is correct
Model Serving provisions a managed, autoscaling HTTP endpoint for a registered model and returns predictions from JSON request payloads. It requires no infrastructure work, handles scaling and versioning, and supports the request-response pattern the external application needs. Creating the endpoint from the Unity Catalog model is the direct way to obtain a REST API.
- ✗
An MLflow experiment tracking run that logs the model's parameters and metrics.
Why it's wrong here
Experiment tracking records metadata about training runs for comparison and reproducibility. It has no serving component and cannot accept or respond to HTTP requests, so it cannot function as an API for an external application. This option confuses the tracking side of MLflow with its deployment capabilities.
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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.