A data scientist has a dataset with many correlated numeric features and wants to reduce its dimensionality while preserving as much variance as possible. Which technique should they use?

PCA (Principal Component Analysis)

A data scientist is working with a dataset that has 200 features and 10,000 rows. The goal is to reduce dimensionality for a linear regression model while preserving as much variance as possible. Which method is most appropriate?

Principal Component Analysis (PCA)

What is the main goal of PCA?

To maximize the variance captured by each principal component

To rank features by their importance

To minimize the number of features by removing correlated ones

To convert categorical features into numeric ones

CertNexusCertified Data Science Practitioner (CDSP)

Domain 3Objective 3

Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

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 16–20

16foundation · easy
A data scientist has a dataset with many correlated numeric features and wants to reduce its dimensionality while preserving as much variance as possible. Which technique should they use?
Select an answer first
17application · medium
A data scientist is working with a dataset that has 200 features and 10,000 rows. The goal is to reduce dimensionality for a linear regression model while preserving as much variance as possible. Which method is most appropriate?
Select an answer first
18foundation · easy
A data scientist wants to reverse the order of characters in a string column, such as turning 'abc' into 'cba'. Which text manipulation technique should they use?
Select an answer first
19foundation · easy
What is the main goal of PCA?
Select an answer first
20foundation · easy
A data scientist wants to visualize high-dimensional data in a 2D scatter plot to identify clusters. Which dimensionality reduction technique is specifically designed for visualization?
Select an answer first

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Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 4)

Questions 16–20

A data scientist has a dataset with many correlated numeric features and wants to reduce its dimensionality while preserving as much variance as possible. Which technique should they use?

A data scientist is working with a dataset that has 200 features and 10,000 rows. The goal is to reduce dimensionality for a linear regression model while preserving as much variance as possible. Which method is most appropriate?

A data scientist wants to reverse the order of characters in a string column, such as turning 'abc' into 'cba'. Which text manipulation technique should they use?

What is the main goal of PCA?

A data scientist wants to visualize high-dimensional data in a 2D scatter plot to identify clusters. Which dimensionality reduction technique is specifically designed for visualization?

Keep studying — 3.0 Performing exploratory data analysis