What is the purpose of removing one feature from a highly correlated pair?

To handle missing values in the features

To increase the variance of the remaining features

To reduce redundancy and multicollinearity

To convert the features into categorical ones

Why might a data scientist split a full name into first, middle, and last names?

To convert names into numeric values

To create features that may have different predictive relationships with the target

To handle missing values in the name field

To reduce the number of features in the dataset

Which statement best describes one-hot encoding?

It creates binary columns for each category, with exactly one column active per observation.

It replaces each category with the mean of the target variable for that category.

It assigns integer labels to categories in alphabetical order.

It creates k-1 binary columns to avoid multicollinearity.

A data scientist is working with a dataset of movies where the 'Title' column contains values like 'Inception (2010)'. The goal is to build a model to predict box office revenue. Which feature engineering step is most appropriate?

Use the 'Title' column as a categorical feature with label encoding

Split the title on spaces and use the first word

Extract the year from the title using a regular expression

CertNexusCertified Data Science Practitioner (CDSP)

Domain 3Objective 3

Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

Part of the 3.0 Performing exploratory data analysis domain, which accounts for 25-36% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

54questions here

11free pages

27concepts

25-36%of the exam

27 Concepts in This Objective

One-hot encoding
Target encoding
Label/Ordinal encoding
Dummy encoding
Effect encoding
Binary encoding
Base-N encoding
Hash encoding
Split features
Text manipulation - Split
Text manipulation - Trim
Text manipulation - Reverse
Manipulate data - Split names
Manipulate data - Extract year from title
Convert dates to useful features
PCA (Principal Component Analysis)
Missing value ratio
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Low-variance filter
Random forest feature importance
High-correlation filter
Backward feature elimination
SVD (Singular Value Decomposition)
Forward feature selection
False discovery rate
Factor analysis
Feature importance methods

Questions 11–15

11foundation · easy
What is the purpose of removing one feature from a highly correlated pair?
Select an answer first
12foundation · easy
A data scientist has a string column 'address' with values like '123 Main St, Springfield'. They want to separate the street address from the city. Which text manipulation technique should they use?
Select an answer first
13foundation · easy
Why might a data scientist split a full name into first, middle, and last names?
Select an answer first
14foundation · easy
Which statement best describes one-hot encoding?
Select an answer first
15application · easy
A data scientist is working with a dataset of movies where the 'Title' column contains values like 'Inception (2010)'. The goal is to build a model to predict box office revenue. Which feature engineering step is most appropriate?
Select an answer first

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