A data analyst is preparing a dataset for a k-nearest neighbors (KNN) model. The 'age' feature ranges from 0 to 90, while the 'income' feature ranges from 20,000 to 150,000. Which technique should be applied to ensure both features contribute equally to the distance calculation?

Feature selection, such as removing the 'income' feature

Data imputation, such as filling missing values with the mean

Feature scaling, such as normalization or standardization

Data binning, such as grouping ages into categories

A data scientist is building a churn prediction model. The dataset contains a feature 'last_purchase_date' and 'signup_date'. The scientist wants to create a feature that represents the recency of the last purchase. What should the scientist do?

Apply a log transformation to the last_purchase_date

Standardize the last_purchase_date and signup_date

Calculate the difference between the current date and the last_purchase_date to create a 'days_since_last_purchase' feature

Create a polynomial feature from the signup_date

A data analyst is preparing data for a principal component analysis (PCA). The dataset contains features measured in different units, such as height in centimeters and weight in kilograms. The analyst wants to ensure that PCA gives equal importance to all features. What should the analyst do?

Remove features with large variances

Apply a log transformation to all features

Normalize all features to a range of [0, 1]

Standardize all features to have zero mean and unit variance

A real estate company wants to build a model to predict property prices. They have addresses of properties and want to include a feature that captures proximity to city centers. The data team has geocoded the addresses and obtained latitude/longitude coordinates. What additional feature engineering step should they perform?

Standardize the latitude and longitude values

Create a polynomial feature from the latitude and longitude

Calculate the distance from each property to the city center coordinates

Apply a log transformation to the latitude and longitude values

A ride-sharing company wants to predict trip duration. They have pickup and drop-off addresses. They have geocoded the addresses and have the coordinates. They also have a feature 'distance' calculated as the straight-line distance. However, the model's performance is poor. The data scientist suspects that the straight-line distance is not capturing the actual road distance. What should the data scientist do?

Apply a log transformation to the straight-line distance

Create a polynomial feature from the straight-line distance

Standardize the straight-line distance and use it as is

Use a routing API to calculate the actual road distance between the coordinates

CompTIADataX

Domain 2Objective 3

Data Enrichment DY0-001 Practice Questions (Page 1)

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 1–5

1foundation · easy
A data analyst is preparing a dataset for a k-nearest neighbors (KNN) model. The 'age' feature ranges from 0 to 90, while the 'income' feature ranges from 20,000 to 150,000. Which technique should be applied to ensure both features contribute equally to the distance calculation?
Select an answer first
2expert · medium
A data scientist is building a churn prediction model. The dataset contains a feature 'last_purchase_date' and 'signup_date'. The scientist wants to create a feature that represents the recency of the last purchase. What should the scientist do?
Select an answer first
3application · medium
A data analyst is preparing data for a principal component analysis (PCA). The dataset contains features measured in different units, such as height in centimeters and weight in kilograms. The analyst wants to ensure that PCA gives equal importance to all features. What should the analyst do?
Select an answer first
4application · medium
A real estate company wants to build a model to predict property prices. They have addresses of properties and want to include a feature that captures proximity to city centers. The data team has geocoded the addresses and obtained latitude/longitude coordinates. What additional feature engineering step should they perform?
Select an answer first
5expert · medium
A ride-sharing company wants to predict trip duration. They have pickup and drop-off addresses. They have geocoded the addresses and have the coordinates. They also have a feature 'distance' calculated as the straight-line distance. However, the model's performance is poor. The data scientist suspects that the straight-line distance is not capturing the actual road distance. What should the data scientist do?
Select an answer first

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Data Enrichment DY0-001 Practice Questions (Page 1)

Questions 1–5

A data analyst is preparing a dataset for a k-nearest neighbors (KNN) model. The 'age' feature ranges from 0 to 90, while the 'income' feature ranges from 20,000 to 150,000. Which technique should be applied to ensure both features contribute equally to the distance calculation?

A data scientist is building a churn prediction model. The dataset contains a feature 'last_purchase_date' and 'signup_date'. The scientist wants to create a feature that represents the recency of the last purchase. What should the scientist do?

A data analyst is preparing data for a principal component analysis (PCA). The dataset contains features measured in different units, such as height in centimeters and weight in kilograms. The analyst wants to ensure that PCA gives equal importance to all features. What should the analyst do?

Keep studying — Modeling, analysis, and outcomes