A data analyst wants to capture a non-linear relationship between a feature 'x' and the target variable 'y' in a linear regression model. Which data transformation would allow the model to fit a curve?

Polynomial transformation, such as adding x^2 as a new feature

Geocoding, such as converting x to coordinates

One-hot encoding, such as creating dummy variables

Standardization, such as z-score scaling

A logistics company has a dataset of customer addresses and wants to visualize delivery routes on a map. Which process should be applied to the addresses to enable this spatial analysis?

Data transformation, applying a log transformation to the address values

Feature engineering, creating a new column with the length of each address string

Data normalization, scaling the addresses to a common range

Geocoding, converting addresses into latitude and longitude coordinates

A data scientist is building a model to predict house prices. The dataset includes 'area' (in square feet), 'bedrooms', and 'bathrooms'. The scientist suspects that the relationship between area and price is non-linear and that the number of bathrooms may interact with area. The scientist also plans to use a linear regression model. What should the scientist do?

Remove the bathrooms feature to avoid multicollinearity

Standardize all features and use them as is

Apply a log transformation to area and use the other features as is

Create a polynomial feature for area (e.g., area^2) and an interaction feature area * bathrooms

A data scientist is building a model to predict customer satisfaction. The dataset includes a feature 'response_time' that is heavily right-skewed. The scientist wants to use a model that assumes normally distributed features. What should the scientist do to the response_time feature?

Apply a log transformation to response_time

Normalize response_time to [0, 1]

Create a polynomial feature from response_time

CompTIADataX

Domain 2Objective 3

Data Enrichment DY0-001 Practice Questions (Page 4)

Part of the Modeling, analysis, and outcomes domain, which accounts for 24% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~16–26 in this domain), expect 3–5 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)

23questions here

5free pages

4concepts

24%of the exam

Questions 16–20

16foundation · easy
A data analyst wants to capture a non-linear relationship between a feature 'x' and the target variable 'y' in a linear regression model. Which data transformation would allow the model to fit a curve?
Select an answer first
17foundation · easy
A data scientist is working with a right-skewed feature and wants to reduce the skewness to better meet the assumptions of a linear regression model. Which data transformation technique is most appropriate?
Select an answer first
18foundation · easy
A logistics company has a dataset of customer addresses and wants to visualize delivery routes on a map. Which process should be applied to the addresses to enable this spatial analysis?
Select an answer first
19expert · hard
A data scientist is building a model to predict house prices. The dataset includes 'area' (in square feet), 'bedrooms', and 'bathrooms'. The scientist suspects that the relationship between area and price is non-linear and that the number of bathrooms may interact with area. The scientist also plans to use a linear regression model. What should the scientist do?
Select an answer first
20expert · medium
A data scientist is building a model to predict customer satisfaction. The dataset includes a feature 'response_time' that is heavily right-skewed. The scientist wants to use a model that assumes normally distributed features. What should the scientist do to the response_time feature?
Select an answer first

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Data Enrichment DY0-001 Practice Questions (Page 4)

Questions 16–20

A data analyst wants to capture a non-linear relationship between a feature 'x' and the target variable 'y' in a linear regression model. Which data transformation would allow the model to fit a curve?

A data scientist is working with a right-skewed feature and wants to reduce the skewness to better meet the assumptions of a linear regression model. Which data transformation technique is most appropriate?

A logistics company has a dataset of customer addresses and wants to visualize delivery routes on a map. Which process should be applied to the addresses to enable this spatial analysis?

A data scientist is building a model to predict customer satisfaction. The dataset includes a feature 'response_time' that is heavily right-skewed. The scientist wants to use a model that assumes normally distributed features. What should the scientist do to the response_time feature?

Keep studying — Modeling, analysis, and outcomes