Databricks-ML-Assoc Model Development Practice Question
A data scientist is training a scikit-learn model on a Databricks cluster using MLflow. To enable automatic logging of parameters, metrics, and models, they call mlflow.sklearn.autolog() before fitting the model. After the run completes, they notice that the model artifact is stored in the run's artifact location but is not registered in the MLflow Model Registry. What is the most likely reason for the model not being registered?
⚠ Common exam trap
The trap here is assuming that autologging includes model registration, when in fact registration is a separate step that requires an explicit model name.
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
✓
The autolog() function logs the model but does not register it; registration requires either specifying registered_model_name in autolog() or manually registering the model.
Autologging in MLflow captures parameters, metrics, and models but does not automatically register them in the Model Registry. To register, you must specify a registered model name either when calling autolog or when logging the model. Without that, the model remains only as an artifact in the run. This is a common point of confusion for practitioners expecting end-to-end automation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model registration failed because the cluster does not have the necessary permissions to write to the Model Registry.
Why it's wrong here
Permissions could be a factor, but the scenario does not indicate any permission errors. The more fundamental reason is that autolog does not register models by default. Even with full permissions, the model would not be registered unless explicitly requested. Therefore, permissions are not the primary cause here.
- ✗
The autolog() function does not log models for scikit-learn; it only logs parameters and metrics.
Why it's wrong here
This is incorrect because mlflow.sklearn.autolog() does log the trained model artifact by default. It captures the model in its native format and logs it under the run's artifacts. The absence of registration is not due to autolog failing to log the model; it is due to the separate step required for registration. Registration is not automatic unless explicitly configured.
- ✓
The autolog() function logs the model but does not register it; registration requires either specifying registered_model_name in autolog() or manually registering the model.
Why this is correct
This is correct because mlflow.sklearn.autolog() logs the model artifact to the run but does not automatically register it in the Model Registry unless the registered_model_name argument is provided. By default, autolog only logs to the experiment's artifact store. To register, one must either pass registered_model_name to autolog or call mlflow.register_model after logging.
- ✗
The model was not registered because the MLflow run was not associated with a registered model name.
Why it's wrong here
While a registered model name is required for registration, the issue is not that the run lacks a name. The run itself does not need to be associated with a registered model name for autologging to work. Registration is a separate action that must be triggered, either manually or via the registered_model_name parameter in autolog or log_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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