Databricks-ML-Assoc Model Deployment Practice Question
A data scientist has trained a scikit-learn model and wants to expose it for real-time inference through Databricks Model Serving. The model is currently logged as an MLflow run artifact but has not been registered anywhere. What must the data scientist do before creating the serving endpoint?
⚠ Common exam trap
The trap here is treating a logged MLflow artifact as directly deployable, when Model Serving actually requires a registered model version to point the endpoint at.
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
✓
Register the model in Unity Catalog and note the model name and version.
Databricks Model Serving deploys registered model versions, referencing a Unity Catalog model name and version. Logging a model during training stores artifacts but does not make the model deployable until it is registered. Once registered, the endpoint can be created against that version, and the serving layer uses the stored signature and environment to build the container. Raw file uploads, format conversions, and scheduled jobs do not satisfy this prerequisite.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the model to an ONNX file for cross-platform compatibility.
Why it's wrong here
Converting to ONNX is an optional optimization for certain runtimes and is not a prerequisite for Databricks Model Serving, which natively supports MLflow Python models including scikit-learn flavors. Performing an ONNX conversion adds work and can lose flavor-specific behavior without satisfying the actual requirement, which is to have a registered model version the endpoint can reference.
- ✓
Register the model in Unity Catalog and note the model name and version.
Why this is correct
Databricks Model Serving creates endpoints from registered model versions, referencing a three-level Unity Catalog name such as catalog.schema.model and a version number. Registering the logged model makes the artifacts, signature, and environment metadata available for the serving layer to build and deploy. Without a registered version, there is no model entity the endpoint can point to, so registration is the required prerequisite.
- ✗
Export the model to a pickle file and upload it to DBFS.
Why it's wrong here
Uploading a pickle file to DBFS does not create the model version that Model Serving expects. Databricks Model Serving sources models from Unity Catalog (or the workspace Model Registry), not from arbitrary DBFS paths. A raw pickle also lacks the MLflow metadata, signature, and environment that the serving layer needs to build the container and validate requests, so this approach would not enable endpoint creation.
- ✗
Create a Databricks job that runs the model on a schedule.
Why it's wrong here
A scheduled job performs batch or streaming inference on a cluster; it does not create a real-time serving endpoint. Model Serving provides a REST API with autoscaling replicas, which is architecturally different from job-based execution. Creating a job would not expose the model for low-latency requests and does not fulfill the registration requirement that endpoint creation depends on.
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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.