Databricks-ML-Assoc Model Development Practice Question
Exhibit
import mlflow mlflow.sklearn.log_model(model, 'model', registered_model_name='my_model')
Refer to the exhibit. What is the effect of using the 'registered_model_name' parameter in the 'log_model' function?
⚠ Common exam trap
Candidates often believe that registering a model requires a separate, manual step after training, failing to realize that the 'registered_model_name' parameter automates this during the logging process.
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
✓
It registers a new version of the model in the MLflow Model Registry.
Including 'registered_model_name' automatically registers the model in the MLflow Model Registry as part of the logging process. This simplifies the deployment pipeline by eliminating the need to manually promote the model later. It creates a new version entry directly in the registry, ensuring that the model is immediately available for staging or production assignment, which is a best practice for streamlined machine learning operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It restricts access to the model to the current user only.
Why it's wrong here
Registration in the Model Registry is a workspace-level action. It does not set permissions based on the user who performed the logging. Access control is managed separately through the Databricks permissions system, which governs who can view, edit, or manage the registered model artifacts within the registry service.
- ✗
It automatically promotes the model to the 'Production' stage.
Why it's wrong here
Registration creates a version, but it does not move the model to the 'Production' stage. Stages must be explicitly transitioned after validation. Automatic promotion would be a significant safety risk, as it could push an unvalidated or poorly performing model into production without human oversight or automated testing gates.
- ✓
It registers a new version of the model in the MLflow Model Registry.
Why this is correct
Passing the 'registered_model_name' parameter triggers an automated entry into the registry. If the model name already exists, it creates a new version; if it does not, it creates a new registered model. This is the standard, efficient way to integrate logging with artifact management in the Databricks environment.
- ✗
It forces the model to be saved in a specific S3 bucket.
Why it's wrong here
The 'registered_model_name' parameter controls the MLflow Model Registry entry, not the physical storage location. Storage is handled by the underlying Databricks workspace configuration (often DBFS or a managed cloud bucket). It does not provide the capability to override workspace storage settings during the registration process.
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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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