Databricks Exam Databricks-Machine-Learning-Professional Topic 3 Question 37 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Professional exam

Question #: 37
Topic #: 3

[All Databricks-Machine-Learning-Professional Questions]

A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.

Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

Amlflow.load_model(model_uri)

Bclient.list_artifacts(run_id)['feature-importances.csv']

Cmlflow.sklearn.load_model(model_uri)

DThis can only be viewed in the MLflow Experiments UI

Eclient.pyfunc.load_model(model_uri)

Show Suggested Answer

Suggested Answer: A

by Chantell at Mar 19, 2025, 04:30 PM

Limited Time Offer

25%

Off

Get Premium Databricks-Machine-Learning-Professional Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Monroe

6 days ago

I think the correct answer is C) mlflow.sklearn.load_model(model_uri)

upvoted 0 times

...

Jutta

9 days ago

Option C looks like the correct answer. You can use `mlflow.sklearn.load_model()` to restore the original model object and access the `feature_importances_`.

upvoted 0 times

...