Free Practice Databricks Certified-Machine-Learning-Professional Exam Questions 2025

Stay ahead with 100% Free Certified Machine Learning Professional Certified-Machine-Learning-Professional Dumps Practice Questions

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Total 60 Questions | Updated On: Apr 08, 2025

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Question 1

Which of the following is a benefit of logging a model signature with an MLflow model?

A : The model will have a unique identifier in the MLflow experiment

B : The schema of input data can be validated when serving models

C : The model can be deployed using real-time serving tools

D : The model will be secured by the user that developed it

E : The schema of input data will be converted to match the signature

Answer: E

Question 2

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.

Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A :

B : It is not possible to deploy a scikit-learn model on a Spark DataFrame.

C :

D :

E :

Answer: D

Question 3

Which of the following statements describes streaming with Spark as a model deployment strategy?

A : The inference of batch processed records as soon as a trigger is hit

B : The inference of all types of records in real-time

C : The inference of batch processed records as soon as a Spark job is run

D : The inference of incrementally processed records as soon as trigger is hit

E : The inference of incrementally processed records as soon as a Spark job is run

Answer: E

Question 4

Which of the following statements describes streaming with Spark as a model deployment strategy?

A : The inference of batch processed records as soon as a trigger is hit

B : The inference of all types of records in real-time

C : The inference of batch processed records as soon as a Spark job is run

D : The inference of incrementally processed records as soon as trigger is hit

E : The inference of incrementally processed records as soon as a Spark job is run

Answer: E

Question 5

Which of the following is a benefit of logging a model signature with an MLflow model?

A : The model will have a unique identifier in the MLflow experiment

B : The schema of input data can be validated when serving models

C : The model can be deployed using real-time serving tools

D : The model will be secured by the user that developed it

E : The schema of input data will be converted to match the signature

Answer: E

Page: 1 / 12

Total 60 Questions | Updated On: Apr 08, 2025

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