A team is cleaning a dataset for a binary classification task. They notice that the 'age' column has some values that are negative (e.g., -5) and some values that are above 120. What is the most appropriate way to handle these values?

Cap the age values at a reasonable maximum (e.g., 100) and set negative values to 0.

Replace the invalid values with the mean age of the dataset.

Remove the rows with invalid age values from the dataset.

Treat the invalid values as missing and use a proper imputation method, such as median imputation, after investigating the source of the errors.

A data scientist is preparing a dataset for a linear regression model. The dataset contains a categorical feature 'education_level' with values: 'High School', 'Bachelor', 'Master', 'PhD'. What is the most appropriate way to encode this feature?

Use target encoding, replacing each category with the mean of the target variable for that category.

Use ordinal encoding, mapping 'High School' to 1, 'Bachelor' to 2, 'Master' to 3, 'PhD' to 4.

Use label encoding, assigning 0, 1, 2, 3 to the categories.

Use one-hot encoding, creating four binary columns.

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Domain 4Objective 1

Challenges in Data Preparation 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 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

8concepts

Questions 21–25

21foundation · easy
A dataset contains customer records where some entries have the age field left blank. Which data quality issue does this represent?
Select an answer first
22application · medium
A team is cleaning a dataset for a binary classification task. They notice that the 'age' column has some values that are negative (e.g., -5) and some values that are above 120. What is the most appropriate way to handle these values?
Select an answer first
23foundation · easy
Which approach to handling imbalanced data involves modifying the learning algorithm to penalize misclassifications of the minority class more heavily?
Select an answer first
24foundation · easy
Which data split is used to tune hyperparameters and make decisions about model selection during the development process?
Select an answer first
25application · medium
A data scientist is preparing a dataset for a linear regression model. The dataset contains a categorical feature 'education_level' with values: 'High School', 'Bachelor', 'Master', 'PhD'. What is the most appropriate way to encode this feature?
Select an answer first

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Challenges in Data Preparation CT-AI Practice Questions (Page 5)

Questions 21–25

A dataset contains customer records where some entries have the age field left blank. Which data quality issue does this represent?

A team is cleaning a dataset for a binary classification task. They notice that the 'age' column has some values that are negative (e.g., -5) and some values that are above 120. What is the most appropriate way to handle these values?

Which approach to handling imbalanced data involves modifying the learning algorithm to penalize misclassifications of the minority class more heavily?

Which data split is used to tune hyperparameters and make decisions about model selection during the development process?

A data scientist is preparing a dataset for a linear regression model. The dataset contains a categorical feature 'education_level' with values: 'High School', 'Bachelor', 'Master', 'PhD'. What is the most appropriate way to encode this feature?

Keep studying — Domain 4: ML - Data