DA0-002 Data Concepts and Environments Practice Question
A data scientist is building a machine learning model to predict customer churn. The dataset includes both numerical features (age, income) and categorical features (gender, marital status). Which data concept describes the process of converting categorical features into numerical values that can be used by the algorithm?
⚠ Common exam trap
CompTIA often tests the distinction between encoding and feature scaling, where candidates mistakenly think scaling applies to categorical data, but scaling only adjusts numeric ranges and cannot convert text labels to numbers.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Encoding
Encoding is the correct data concept because it transforms categorical features (like gender and marital status) into numerical representations (e.g., one-hot encoding, label encoding) that machine learning algorithms can process. Unlike feature scaling or dimensionality reduction, encoding directly addresses the incompatibility of non-numeric data with mathematical model operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data sampling
Why it's wrong here
Sampling selects a subset of rows.
- ✓
Encoding
Why this is correct
Encoding converts categories to numbers, e.g., one-hot encoding.
- ✗
Feature scaling
Why it's wrong here
Scaling adjusts numerical ranges.
- ✗
Dimensionality reduction
Why it's wrong here
Reduction decreases feature count.
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