What is the goal of feature selection?

To increase the number of features in the dataset

To choose the most relevant features that contribute to model performance

To transform categorical features into numerical ones

To handle missing values in the dataset

A data scientist is working on a dataset with 500 features for a classification task. They suspect many features are redundant or irrelevant. They plan to use a linear SVM model. Which feature selection approach is most appropriate?

Use L2 regularization (Ridge) to shrink all coefficients evenly.

Remove features with low variance without considering the target.

Use L1 regularization (Lasso) to shrink irrelevant feature coefficients to zero.

Use a random forest to rank features and keep the top 50.

A data analyst is working on a dataset for predicting customer lifetime value. They have a 'purchase_date' column and a 'customer_signup_date' column. Which feature engineering step would most likely improve model performance?

Drop both date columns because models cannot handle datetime data.

Create a 'purchase_month' feature from purchase_date and drop signup_date.

Convert both date columns to Unix timestamps and use them as-is.

Create a 'days_since_signup' feature by subtracting signup_date from purchase_date.

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Domain 4Objective 2

Hands-On Exercise: Data Preparation for ML CT-AI Practice Questions (Page 1)

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 1–5

1foundation · easy
What is the goal of feature selection?
Select an answer first
2foundation · easy
Which of the following is a typical step in the data preparation process?
Select an answer first
3application · medium
A data scientist is working on a dataset with 500 features for a classification task. They suspect many features are redundant or irrelevant. They plan to use a linear SVM model. Which feature selection approach is most appropriate?
Select an answer first
4foundation · easy
Which technique is used to encode categorical variables into numerical form?
Select an answer first
5application · medium
A data analyst is working on a dataset for predicting customer lifetime value. They have a 'purchase_date' column and a 'customer_signup_date' column. Which feature engineering step would most likely improve model performance?
Select an answer first

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Hands-On Exercise: Data Preparation for ML CT-AI Practice Questions (Page 1)

Questions 1–5

What is the goal of feature selection?

Which of the following is a typical step in the data preparation process?

A data scientist is working on a dataset with 500 features for a classification task. They suspect many features are redundant or irrelevant. They plan to use a linear SVM model. Which feature selection approach is most appropriate?

Which technique is used to encode categorical variables into numerical form?

A data analyst is working on a dataset for predicting customer lifetime value. They have a 'purchase_date' column and a 'customer_signup_date' column. Which feature engineering step would most likely improve model performance?

Keep studying — Domain 4: ML - Data