What is the primary purpose of the string split operation in feature engineering?

To separate a string into multiple components based on a delimiter

To convert a string to a numeric value

To remove unwanted characters from the beginning and end of a string

To reverse the order of characters in a string

What is the typical output of extracting the year from a title like 'The Matrix (1999)'?

A boolean indicating if the year is present

The full title with the year removed

A data scientist is building a model to predict loan default and needs to explain the model's predictions to stakeholders. The model is a gradient boosting machine. The data scientist wants to quantify each feature's contribution to individual predictions. Which method is most appropriate?

Random forest feature importance

Why are features with low variance often removed in feature selection?

They are highly correlated with other features.

They have too many missing values.

They carry little information because they are nearly constant.

They are not linearly related to the target.

CertNexusCertified Data Science Practitioner (CDSP)

Domain 3Objective 3

Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 7)

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 31–35

31foundation · easy
What is the primary purpose of the string split operation in feature engineering?
Select an answer first
32foundation · easy
What is the typical output of extracting the year from a title like 'The Matrix (1999)'?
Select an answer first
33expert · hard
A data scientist is building a model to predict loan default and needs to explain the model's predictions to stakeholders. The model is a gradient boosting machine. The data scientist wants to quantify each feature's contribution to individual predictions. Which method is most appropriate?
Select an answer first
34foundation · easy
Why are features with low variance often removed in feature selection?
Select an answer first
35application · medium
A data scientist is working with a dataset of psychological survey responses with 50 questions. The goal is to reduce dimensionality by identifying underlying latent factors that explain the correlations among the questions. Which method is most appropriate?
Select an answer first

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