DA0-002 Visualization and Reporting Practice Question
A data scientist has a dataset with 50 variables and wants to identify clusters of similar observations. Which visualization technique is most suitable for reducing dimensionality to 2D while preserving cluster structure?
⚠ Common exam trap
The trap is choosing a visualization that shows all variables (pairplot, parallel coordinates) instead of one that actually reduces dimensionality to 2D while preserving cluster structure — only PCA scatter does both.
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
✓
Scatter plot of first two principal components
A scatter plot of the first two principal components applies PCA to project 50-dimensional data onto two axes that capture the most variance, preserving the global structure and cluster separation. It is the standard dimensionality-reduction visualization for cluster exploration because distances between points in PC space approximate distances in the original feature space. Other listed techniques either show pairwise relationships or all dimensions without reducing to 2D.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Heatmap of correlations
Why it's wrong here
A correlation heatmap shows pairwise linear relationships between the 50 variables; it does not project observations into a two-dimensional space, so cluster structure remains invisible. It is tempting because heatmaps do reveal redundancy among variables, and would suit feature-selection screening before clustering, but not dimensionality reduction for visualising observation groups.
- ✗
Scatter matrix (pairplot)
Why it's wrong here
A scatter matrix plots every variable pair in the original high-dimensional space, producing 1,225 panels rather than a single 2D embedding, so cluster structure across 50 variables is not preserved compactly. It is tempting for pairwise relationship exploration, but dimensionality reduction to 2D requires PCA or t-SNE.
- ✗
Parallel coordinates plot
Why it's wrong here
Parallel coordinates plot all 50 axes side by side without reducing dimensionality, so clusters appear as crossing line bundles rather than a 2D embedding. It is tempting for inspecting multivariate patterns, but preserving cluster structure in two dimensions requires a projection method such as PCA or t-SNE.
- ✓
Scatter plot of first two principal components
Why this is correct
A scatter plot of the first two principal components projects the 50-variable dataset onto two orthogonal axes of greatest variance, preserving cluster separation. This satisfies the dimensionality-reduction constraint: PCA compresses correlated variables into uncorrelated components, letting similar observations group visibly in 2D.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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