What is a primary drawback of using k-fold cross-validation compared to a single train-validation split?

It reduces the amount of data available for training.

It provides a less reliable estimate of model performance.

It requires more computational resources because the model is trained multiple times.

It introduces bias because the validation sets are not random.

Which of the following is a key advantage of k-fold cross-validation over a single train-validation split?

It reduces the variance of the performance estimate.

It guarantees the model will perform well on unseen data.

It reduces the computational cost of training.

It eliminates the need for a test set.

DatabricksCertified Machine Learning Associate

Domain 3Objective 3

Training Pipelines and Cross-Validation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Part of the Section 3: Model Development domain, which makes up ~23% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–14 in this domain), expect 2–4 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

9concepts

Questions 11–15

11foundation · easy
What is a primary drawback of using k-fold cross-validation compared to a single train-validation split?
Select an answer first
12foundation · easy
Which of the following is a key advantage of k-fold cross-validation over a single train-validation split?
Select an answer first
13application · easy
A machine learning engineer runs a grid search over a RandomForestClassifier with 3 values for n_estimators and 4 values for max_depth, using 5-fold cross-validation. How many total model fits will be performed?
Select an answer first
14foundation · easy
In scikit-learn, which class is used to perform grid search with cross-validation to find the best hyperparameters?
Select an answer first
15foundation · easy
In a training pipeline, which stage is responsible for converting raw data into a format suitable for the model, such as scaling numeric features or encoding categorical variables?
Select an answer first

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Training Pipelines and Cross-Validation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Questions 11–15

What is a primary drawback of using k-fold cross-validation compared to a single train-validation split?

Which of the following is a key advantage of k-fold cross-validation over a single train-validation split?

A machine learning engineer runs a grid search over a RandomForestClassifier with 3 values for n_estimators and 4 values for max_depth, using 5-fold cross-validation. How many total model fits will be performed?

In scikit-learn, which class is used to perform grid search with cross-validation to find the best hyperparameters?

In a training pipeline, which stage is responsible for converting raw data into a format suitable for the model, such as scaling numeric features or encoding categorical variables?

Keep studying — Section 3: Model Development