Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer wants to register a model in the Databricks Model Registry using MLflow. They have already trained a model and logged it with MLflow. Which method should they use to register the model programmatically?
⚠ Common exam trap
Many exam-takers confuse logging a model with registering it, or thinking that creating a registered model name is sufficient.
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)
To register a model programmatically in the Databricks Model Registry, the engineer should use mlflow.register_model, which creates a new model version linked to the logged run. This is the direct method for registration. The other options either create a registered model without a version, log the model without registering, or save the model locally without registry integration. Thus, mlflow.register_model is the appropriate choice.
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.register_model(model_uri, name)
Why this is correct
mlflow.register_model is the correct function to register a model version in the Model Registry. It takes the model URI (e.g., 'runs:/run_id/model') and the desired registered model name. This creates a new model version and associates it with the run, enabling versioning and stage transitions. It is the standard programmatic way to register models in Databricks.
- ✗
mlflow.log_model(model, name)
Why it's wrong here
mlflow.log_model logs a model as an artifact within a run but does not register it in the Model Registry. It stores the model files but does not create a registered model or version. To register, an additional step is needed to promote the logged model to the registry, such as using mlflow.register_model.
- ✗
mlflow.create_registered_model(name)
Why it's wrong here
mlflow.create_registered_model creates a new registered model entry in the Model Registry but does not create a version from an existing run. It is used to initialize a model name before adding versions. Without also creating a version, the model would not be linked to the trained artifacts, so this alone does not register the model.
- ✗
mlflow.save_model(model, path)
Why it's wrong here
mlflow.save_model saves a model to a local path or a specified directory, but it does not interact with the Model Registry. It is typically used for persisting a model outside of MLflow tracking. It does not create a registered model or version, so it is not suitable for programmatic registration in Databricks.
About these practice questions
One of 319 original Databricks-ML-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.