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Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Machine-Learning-Associate
Exam Name:
Databricks Certified Machine Learning Associate Exam
Certification:
Vendor:
Questions:
74
Last Updated:
Apr 3, 2025
Exam Status:
Stable
Databricks Databricks-Machine-Learning-Associate

Databricks-Machine-Learning-Associate: ML Data Scientist Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Machine Learning Associate Exam Questions and Answers

Question 1

A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model bycomparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model.

Which of the following possible explanations for this difference is invalid?

Options:

A.

The second model is much more accurate than the first model

B.

The data scientist failed to exponentiate the predictions in the second model prior tocomputingthe RMSE

C.

The datascientist failed to take the logof the predictions in the first model prior to computingthe RMSE

D.

The first model is much more accurate than the second model

E.

The RMSE is an invalid evaluation metric for regression problems

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Question 2

A machine learning engineer is trying to scale a machine learning pipelinepipelinethat contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to theestimatorparameter and then placing the updated cv object as the final stage of thepipelinein place of the original model.

Which of the following is a negative consequence of the approach suggested by the colleague?

Options:

A.

The model will take longerto train for each unique combination of hvperparameter values

B.

The feature engineering stages will be computed using validation data

C.

The cross-validation process will no longer be

D.

The cross-validation process will no longer be reproducible

E.

The model will be refit one more per cross-validation fold

Question 3

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library'sfminoperation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with theobjective_functionbeing passed as an argument tofmin.

They use the following code block to create theobjective_function:

Which of the following changes does the data scientist need to make to theirobjective_functionin order to produce a more accurate model?

Options:

A.

Add test set validation process

B.

Add a random_state argument to the RandomForestRegressor operation

C.

Remove the mean operation that is wrapping the cross_val_score operation

D.

Replace the r2 return value with -r2

E.

Replace the fmin operation with the fmax operation