Important Databricks Machine Learning Professional Exam Questions

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Databricks Certified Machine Learning Professional Databricks Machine Learning Professional Exam

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Exam Name: Databricks Certified Machine Learning Professional
Registration Code: Databricks-Machine-Learning-Professional
Related Certification: Databricks Machine Learning Professional Certification
Exam Audience:

Total Questions

60

Last Updated

28-08-2025

Exam Duration

120 MINUTES

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Question: 1

Which of the following is a simple, low-cost method of monitoring numeric feature drift?

Question: 2

A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.

They are using the following code block:

 Exam Question 2 Exhibit 1

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

Question: 3

A machine learning engineer has created a webhook with the following code block:

 Exam Question 3 Exhibit 1

Which of the following code blocks will trigger this webhook to run the associate job?

A)

 Exam Question 3 Exhibit 2

B)

 Exam Question 3 Exhibit 3

C)

 Exam Question 3 Exhibit 4

D)

 Exam Question 3 Exhibit 5

E)

 Exam Question 3 Exhibit 6

Question: 4

A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".

Which of the following lines of code can they use to register the model to the MLflow Model Registry?

Question: 5

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?

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