Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer has a registered model and wants to expose it as a REST endpoint that their application can call for real-time predictions. They need the endpoint to be created and managed natively within Databricks, with the ability to enable scale-to-zero during idle periods. Which Databricks capability should they use?
⚠ Common exam trap
The trap here is assuming that any Databricks compute that can run Python can also host a production-grade model endpoint, when only Model Serving provides managed REST inference.
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, creating an endpoint that serves the registered model and configuring the served entity with scale-to-zero enabled.
Databricks Model Serving is the native mechanism for deploying registered models as REST endpoints with managed lifecycle, authentication, and optional scale-to-zero. A Flask process on a job cluster, registry webhooks, or a SQL warehouse cannot provide a stable, secure, autoscaling prediction API, so they fail the scenario's requirements for native management and idle shutdown.
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 Model Registry webhooks that notify an external service whenever a model version is registered, letting that service host the model.
Why it's wrong here
Webhooks only emit event notifications; they do not host or serve a model. Using them would require building and operating an external inference service, which contradicts the requirement to manage the endpoint natively in Databricks. Webhooks are useful for triggering CI/CD or retraining workflows, but they provide no prediction API or scale-to-zero behavior on their own.
- ✗
A Databricks job that runs a Python script loading the model and starting a Flask web server on a driver node, exposed via a public URL.
Why it's wrong here
Running Flask on a job cluster driver is not a supported production serving pattern: the URL is not stable, the cluster terminates, and there is no built-in authentication or autoscaling. It also cannot scale to zero in a managed way. This approach lacks the reliability, security, and lifecycle management that a native serving endpoint provides.
- ✓
Databricks Model Serving, creating an endpoint that serves the registered model and configuring the served entity with scale-to-zero enabled.
Why this is correct
Databricks Model Serving provides managed REST endpoints for registered models and supports scale-to-zero, which shuts down compute when there is no traffic and cold-starts on the next request. This satisfies both the native management and idle-cost requirements. The endpoint exposes a standard REST interface and integrates with Unity Catalog for permissions and lineage.
- ✗
A SQL warehouse with a user-defined function that loads the model artifact and returns predictions for incoming queries.
Why it's wrong here
SQL warehouses execute SQL workloads and cannot load arbitrary Python model artifacts as a general inference engine, nor do they expose a model prediction REST API with cold-start semantics. While some AI functions exist, they do not replace Model Serving for custom registered models. This choice misuses the warehouse and cannot deliver the required endpoint behavior.
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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
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