An ML team is working on a time-series dataset of daily website traffic over 2 years. They need to build a model to forecast future traffic. Which data splitting approach is most appropriate?

Split chronologically: train on the first 18 months, validate on the next 3 months, test on the last 3 months.

Randomly shuffle the data and split into 70% training, 15% validation, and 15% test.

Use the first year for training and the second year for both validation and test.

Stratified split by day of the week into 80% training and 20% test.

Why is it important to split data into training, validation, and test sets?

To ensure all data is used for training

To reduce the training time of the model

To increase the size of the dataset

To evaluate the model's performance on unseen data and prevent overfitting

What is the typical purpose of the validation set in a data split?

To provide a final unbiased evaluation of the model

To tune hyperparameters and make model selection decisions

To augment the training data with synthetic samples

To train the model on a larger dataset

When implementing a data preparation workflow, why is it important to apply the same transformations to the test set as the training set?

To reduce the number of features in the test set

To maintain consistency and ensure the model sees data in the same format during evaluation

To ensure the test set is larger than the training set

To increase the accuracy of the model on the test set

In a hands-on data preparation workflow using a practical ML dataset, which step typically comes first?

Exploring the data to understand its structure and quality

Evaluating the model's performance

Splitting the data into training and test sets

Training the machine learning model

Certified Tester AI Testing

Domain 4Objective 2

Hands-On Exercise: Data Preparation for ML CT-AI Practice Questions (Page 5)

Part of the Domain 4: ML - Data domain, which makes up ~12% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–5 in this domain), expect 1–1 from this objective — we provide 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

6concepts

Questions 21–25

21application · medium
An ML team is working on a time-series dataset of daily website traffic over 2 years. They need to build a model to forecast future traffic. Which data splitting approach is most appropriate?
Select an answer first
22foundation · easy
Why is it important to split data into training, validation, and test sets?
Select an answer first
23foundation · easy
What is the typical purpose of the validation set in a data split?
Select an answer first
24foundation · easy
When implementing a data preparation workflow, why is it important to apply the same transformations to the test set as the training set?
Select an answer first
25foundation · easy
In a hands-on data preparation workflow using a practical ML dataset, which step typically comes first?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

No more pagesBack to CT-AI

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by ISTQB. “CT-AI” is a trademark of its owner, used for identification only.