DA0-002 Data Analysis Practice Question
A data analyst is working with a dataset that includes a categorical variable 'product_category' with 50 unique values. The analyst wants to reduce dimensionality before clustering. Which technique should the analyst use?
⚠ Common exam trap
The trap here is assuming PCA can be applied to any data after encoding, but PCA on one-hot encoded data may not effectively reduce dimensionality and can lose interpretability.
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
✓
Multiple correspondence analysis (MCA)
Multiple correspondence analysis is a dimensionality reduction technique tailored for categorical data. It converts categories into numerical dimensions that capture the underlying structure, making it suitable for clustering. Unlike one-hot encoding, it reduces rather than expands the feature space. PCA and factor analysis are designed for continuous data, so they are not directly applicable to a categorical variable with many levels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Multiple correspondence analysis (MCA)
Why this is correct
Multiple correspondence analysis is specifically designed to reduce dimensionality of categorical data by transforming categories into a lower-dimensional numerical space. It captures associations between categories and can handle variables with many levels. This makes it ideal for the analyst's goal of reducing the 50 product categories before clustering. MCA preserves the categorical structure while enabling the use of distance-based clustering algorithms.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates a binary column for each category, increasing dimensionality from 50 to 50 new features. This expands the feature space and can lead to the curse of dimensionality, making clustering less effective. While it is a common preprocessing step, it does not reduce dimensionality; it increases it. Therefore, it is not appropriate for the analyst's goal of dimensionality reduction before clustering.
- ✗
Factor analysis
Why it's wrong here
Factor analysis is typically used for continuous variables to uncover latent factors. It assumes linear relationships and normal distributions, which are not applicable to categorical data. Applying it to product_category would violate its assumptions and likely produce meaningless factors. While related to PCA, it is not suited for dimensionality reduction of categorical variables. Therefore, it is not the correct choice here.
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Principal component analysis (PCA)
Why it's wrong here
PCA is designed for continuous numerical data and assumes linear relationships. Applying PCA directly to a categorical variable like product_category is inappropriate because the categories have no inherent numerical order. While one could first encode the categories and then apply PCA, that adds complexity and may not capture categorical associations well. Thus, PCA is not the best technique for reducing dimensionality of a purely categorical variable.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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