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Vendor: Databricks
Exam Code: DATABRICKS-MACHINE-LEARNING-PROFESSIONAL
Exam Name: Databricks Certified Machine Learning Professional
Certification Provider: Databricks
Total Questions: 60 Q&A ( View Details)
Updated on: Jul 05, 2026
Note: Product instant download. Please sign in and click My account to download your product.A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model.
They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a
pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?
A. The pyfunc model can be used to deploy models in a parallelizable fashion
B. The same preprocessing logic will automatically be applied when calling fit
C. The same preprocessing logic will automatically be applied when calling predict
D. This approach has no impact when loading the logged pyfunc model for downstream deployment
E. There is no longer a need for pipeline-like machine learning objects
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df. Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
A machine learning engineer has created a webhook with the following code block:

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

A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
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