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
Data sampling selects a subset of records, for instance for training or testing; it does not transform gender or marital status into numeric form. Encoding performs that conversion. Sampling would be the right concept when the dataset is too large to process or needs a representative training split.
- ✓
Encoding
Why this is correct
Encoding maps categorical values such as gender and marital status into numeric representations, for example one-hot or ordinal vectors, which the algorithm can process. Numerical features like age and income need no conversion, so encoding is the concept that addresses the categorical constraint.
- ✗
Feature scaling
Why it's wrong here
Feature scaling rescales numeric values, for example standardisation or min-max normalisation, and leaves categorical text untouched. Encoding, such as one-hot or ordinal encoding, converts gender and marital status into numbers. Scaling is correct when numeric features differ widely in magnitude.
- ✗
Dimensionality reduction
Why it's wrong here
Dimensionality reduction compresses the feature space (for example via PCA) rather than recoding categories into numbers, so it cannot convert gender or marital status into numeric inputs. It is tempting because it also transforms data before modelling, and would be correct when too many correlated features cause overfitting or slow training.
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