Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is preparing a model for deployment using Databricks Model Serving. They need to ensure that the model's input schema is enforced and that the model can be served with a specific version. Which TWO actions should they perform? (Choose two.)
⚠ Common exam trap
The trap here is assuming that registering a model or logging a separate schema artifact automatically enforces input schema in Model Serving, when only the model signature does.
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
✓
Log the model with an input example using mlflow.sklearn.log_model(..., input_example=...)
Logging the model with an input example captures the input schema, which Model Serving uses to validate requests. Specifying the model version when creating the serving endpoint ensures the correct version is deployed. Together, these actions enforce schema and control versioning. The other options manage lifecycle, log artifacts, or scale resources, but do not directly achieve the stated requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable autoscaling on the serving endpoint to handle varying load.
Why it's wrong here
Autoscaling adjusts the number of replicas based on traffic but does not enforce input schema or specify model version. It is an operational feature for scalability and cost management. While useful for production, it does not address the requirements of schema enforcement or version selection. Therefore, it is not a correct action for this scenario.
- ✓
Log the model with an input example using mlflow.sklearn.log_model(..., input_example=...)
Why this is correct
Providing an input example during model logging allows MLflow to infer and store the input schema. This schema is then used by Model Serving to validate incoming requests, ensuring that the data types and structure match what the model expects. This action directly helps enforce the input schema and prevents errors during serving. It is a recommended practice for production deployments.
- ✓
Specify the model version when creating or updating a serving endpoint.
Why this is correct
When creating or updating a Model Serving endpoint, you can specify the exact model version to serve. This ensures that the desired version is deployed, which is crucial when multiple versions exist. It also allows you to control which version receives traffic. This action directly addresses the need to serve a specific version, making it a correct choice for the scenario.
- ✗
Register the model in the MLflow Model Registry and transition it to Production.
Why it's wrong here
Registering and transitioning a model to Production manages its lifecycle stage but does not enforce input schema. Model Serving can serve models from any stage, but schema enforcement comes from the logged model signature. This action is about versioning and stage management, not about validating input data. Therefore, it does not meet the requirement for schema enforcement.
- ✗
Use mlflow.log_artifact() to save a JSON schema file alongside the model.
Why it's wrong here
While you can log a JSON schema as an artifact, Model Serving does not automatically use it to enforce input schema. The schema must be part of the model's signature, typically provided via input_example or signature during logging. Logging a separate artifact does not integrate with the serving layer's validation. Thus, this action does not enforce the input schema for the deployed model.
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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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