Databricks-ML-Assoc ML Workflows Practice Question
A data scientist trains a scikit-learn model in a Databricks notebook and logs it with MLflow. She now needs to promote the exact model artifact to the Databricks Model Registry so that it can be served. Which MLflow API call accomplishes this promotion?
⚠ Common exam trap
The trap here is assuming that logging or saving a model artifact automatically creates a registry entry, when registration is a distinct API call that links an artifact URI to a registered 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
✓
mlflow.register_model(model_uri, name)
Registering a logged model means creating a named entry in the Model Registry that points at a specific run artifact. The mlflow.register_model() function performs exactly that linkage by accepting the run-relative model URI and the desired registered model name, returning a version that can then be transitioned to a stage or alias for serving. Saving, creating an empty registered model, or logging an artifact each perform only one piece of the workflow and leave no registered version.
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.tracking.MlflowClient().create_registered_model(name)
Why it's wrong here
create_registered_model() only creates an empty registered-model container with the given name in the registry. It does not attach any artifact, run, or version, so the trained scikit-learn model is never linked. A subsequent create_model_version() call would be required, which is not what this single API does.
- ✓
mlflow.register_model(model_uri, name)
Why this is correct
mlflow.register_model() takes the run-relative URI of the logged model (for example runs:/<run_id>/model) and a registered model name, then creates or updates the registered model entry in the workspace Model Registry. It is the canonical way to promote an artifact produced by mlflow.sklearn.log_model into the registry, and it returns a ModelVersion object for later stage or alias transitions.
- ✗
mlflow.sklearn.save_model(model, path)
Why it's wrong here
save_model() only serializes the estimator to a local or DBFS filesystem path in MLflow's flavor format. It writes files but does not contact the Model Registry, so no registered model or version is created. After saving, the scientist would still need a separate registration call; this API alone cannot promote the artifact for serving.
- ✗
mlflow.log_artifact(model, artifact_path='model')
Why it's wrong here
log_artifact() copies a file or directory into the current run's artifact store. It records the artifact under the run but does not register a model version, assign a model name, or make the model discoverable in the Model Registry UI. Promotion for serving needs registration, not just artifact logging.
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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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