Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using MLflow on Databricks to log a scikit-learn model. They call mlflow.sklearn.log_model(model, 'model') and then inspect the run. They notice the model artifact is stored, but the run does not appear in the Models page of the workspace. They did not call any model registration function. What is the most likely reason the model is not listed in the Models page?
⚠ Common exam trap
The trap here is assuming that logging a model automatically registers it in the Model Registry and makes it appear in the Models page.
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 model must be registered in the Model Registry using mlflow.register_model() or the model_signing/registration API before it appears in the Models page.
The Models page in Databricks shows models that have been registered in the Model Registry. Logging a model with mlflow.sklearn.log_model() stores the artifact in the run but does not create a registered model. To make the model visible, the data scientist must register it, for example by using mlflow.register_model() or the model registry UI/API.
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 must be registered in the Model Registry using mlflow.register_model() or the model_signing/registration API before it appears in the Models page.
Why this is correct
The Models page in Databricks displays models registered in the Model Registry, not just logged artifacts. Logging a model with mlflow.sklearn.log_model() only stores artifacts in the run; to appear in the Models page, the model must be registered, which creates a model version and associates it with a registered model name.
- ✗
The model must be logged using mlflow.pyfunc.log_model() instead of mlflow.sklearn.log_model() to be visible in the Models page.
Why it's wrong here
Both mlflow.sklearn.log_model() and mlflow.pyfunc.log_model() log models, but neither automatically registers them in the Model Registry. The choice of flavor does not affect whether the model appears in the Models page; registration is the key step. Using a different flavor would not solve the visibility issue.
- ✗
The model artifact is stored in the run but not in the workspace's default model store, so it cannot be displayed.
Why it's wrong here
Databricks uses a managed model store for registered models, but logged models are stored in the run's artifact location. The absence from the Models page is not due to storage location; it is because registration was not performed. The artifact location does not determine visibility in the Models page.
- ✗
MLflow only shows models in the Models page if the run is part of an experiment with a specific tag.
Why it's wrong here
There is no requirement for a specific tag on the experiment or run for a model to appear in the Models page. The Models page is populated by registered models, not by tags. Tags can help organize runs but do not trigger model registration or display.
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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
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.