A data scientist is using cross_val_score to evaluate a linear regression. They notice the scores vary widely across folds. What is the most likely cause and the best next step?

Cross-validation is broken; switch to a single train-test split.

The model is overfitting; reduce the number of features.

The data may not be shuffled or may have a non-random order; use shuffle=True or StratifiedKFold for classification.

The model is underfitting; increase model complexity.

How does k-fold cross-validation make better use of the available data compared to a single train-validation split?

It uses the entire dataset for training in each fold.

It uses every data point for both training and validation across the folds.

It duplicates the dataset to increase the sample size.

It allows the model to see the validation data during training.

What is the purpose of combining grid search with cross-validation?

To train the model on the entire dataset for each hyperparameter combination.

To reduce the number of hyperparameter combinations to test.

To eliminate the need for a separate test set.

To evaluate each hyperparameter combination using multiple train-validation splits, providing a more reliable estimate of performance.

DatabricksCertified Machine Learning Associate

Domain 3Objective 3

Training Pipelines and Cross-Validation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 5)

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 21–25

21foundation · easy
A grid search evaluates 3 hyperparameter combinations using 5-fold cross-validation. How many times is the model fit during this process?
Select an answer first
22application · medium
A data scientist is using cross_val_score to evaluate a linear regression. They notice the scores vary widely across folds. What is the most likely cause and the best next step?
Select an answer first
23foundation · easy
How does k-fold cross-validation make better use of the available data compared to a single train-validation split?
Select an answer first
24foundation · easy
Which scikit-learn function is commonly used to perform cross-validation and return the scores for each fold?
Select an answer first
25foundation · easy
What is the purpose of combining grid search with cross-validation?
Select an answer first

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Training Pipelines and Cross-Validation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 5)

Questions 21–25

A grid search evaluates 3 hyperparameter combinations using 5-fold cross-validation. How many times is the model fit during this process?

A data scientist is using cross_val_score to evaluate a linear regression. They notice the scores vary widely across folds. What is the most likely cause and the best next step?

How does k-fold cross-validation make better use of the available data compared to a single train-validation split?

Which scikit-learn function is commonly used to perform cross-validation and return the scores for each fold?

What is the purpose of combining grid search with cross-validation?

Keep studying — Section 3: Model Development