☎  076 959 6407✉  support@quizcrazepro.co.za
Learn. Practice. Build your future.
BlogHelpContact
G

Google Professional Machine Learning Engineer

280 Questions120 Minutes70% Passing Score▣ Updated: Sep 2026

Question 152 of 280

Single answer
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?
AUse sparse representation in the test set.
BRandomly redistribute the data, with 70% for the training set and 30% for the test set
CApply one-hot encoding on the categorical variables in the test data
DCollect more data representing all categories
Correct Answer: C

Apply one-hot encoding on the categorical variables in the test data

Explanation

The correct answer is highlighted above. Review the wording carefully, then use the next question to continue building your understanding of Google certification topics.

About this practice exam

Review 280 Google questions with answers and explanations. Use the navigation to move through the exam at your own pace.