Databricks Certified Machine Learning Associate · Free Practice Question Medium
Question 3
A data scientist is using 3-fold cross-validation and a specific hyperparameter grid for optimizing model hyperparameters via grid search in a classification problem.
The hyperparameter grid is as follows:
Hyperparameter 1 [4, 6, 7]
Hyperparameter 2 [5, 10]
What is the total number of machine learning models that can be trained simultaneously during this process?
Choose only ONE best answer.
-
A
2
-
B
6
-
C
12
-
D
18
-
E
24
Reveal correct answer
Correct answer: D
Explanation
The total number of machine learning models trained during a 3-fold cross-validation with the given hyperparameter grid is calculated by multiplying the number of hyperparameter combinations by the number of folds.
Hyperparameter combinations:
Hyperparameter 1 has 3 values: [4, 6, 7].
Hyperparameter 2 has 2 values: [5, 10].
Total combinations = 3 × 2 = 6.
Cross-validation folds:
For each hyperparameter combination, 3 models are trained (one per fold).
Total models = 6 combinations × 3 folds = 18.
The term "simultaneously" in the question refers to the total models trained across all folds and hyperparameters, not parallel execution. Thus, the correct answer is 18.
Discussion
Think the marked answer is wrong, or have a better explanation? Share it below — comments appear after review.
