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Databricks Certified Machine Learning Associate Exam

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Databricks Certified Machine Learning Associate Exam Questions and Answers

Questions 1

What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

Options:

A.

Leave-one-out encoding

B.

Target encoding

C.

One-hot encoding

D.

Categorical

E.

String indexing

Questions 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

Questions 3

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.

Which of the following explanations justifies this suggestion?

Options:

A.

One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

B.

One-hot encoding is dependent on the target variable’s values which differ for each apaplication.

C.

One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

D.

One-hot encoding is not a common strategy for representing categorical feature variables numerically.