A data scientist is preparing features for a k-nearest neighbors (KNN) model. The dataset includes 'age' (range 0-100) and 'annual income' (range 15,000-250,000). The scientist notices that the income feature dominates the distance calculations. What should the scientist do to ensure both features contribute equally?

Apply a polynomial transformation to income

Apply standardization to both features

Apply a log transformation to age only

A non-profit organization wants to analyze the geographic distribution of its donors. They have donor addresses but no coordinates. They plan to use a clustering algorithm to identify donor hotspots. What is the first step they should take?

Create a polynomial feature from the address strings

Standardize the address strings

Apply a log transformation to the address strings

Geocode the donor addresses to obtain latitude and longitude

A data analyst is working with a dataset where the target variable is highly skewed and the model is a linear regression. The analyst wants to improve the model's performance by making the target distribution more symmetric. Which data transformation should the analyst apply?

Normalization on the target variable

Polynomial transformation on the target variable

Standardization on the target variable

Log transformation on the target variable

CompTIADataX

Domain 2Objective 3

Data Enrichment DY0-001 Practice Questions (Page 5)

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 21–23

21application · medium
A data scientist is preparing features for a k-nearest neighbors (KNN) model. The dataset includes 'age' (range 0-100) and 'annual income' (range 15,000-250,000). The scientist notices that the income feature dominates the distance calculations. What should the scientist do to ensure both features contribute equally?
Select an answer first
22application · easy
A non-profit organization wants to analyze the geographic distribution of its donors. They have donor addresses but no coordinates. They plan to use a clustering algorithm to identify donor hotspots. What is the first step they should take?
Select an answer first
23application · easy
A data analyst is working with a dataset where the target variable is highly skewed and the model is a linear regression. The analyst wants to improve the model's performance by making the target distribution more symmetric. Which data transformation should the analyst apply?
Select an answer first

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Data Enrichment DY0-001 Practice Questions (Page 5)

Questions 21–23

A non-profit organization wants to analyze the geographic distribution of its donors. They have donor addresses but no coordinates. They plan to use a clustering algorithm to identify donor hotspots. What is the first step they should take?

A data analyst is working with a dataset where the target variable is highly skewed and the model is a linear regression. The analyst wants to improve the model's performance by making the target distribution more symmetric. Which data transformation should the analyst apply?

Keep studying — Modeling, analysis, and outcomes