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?
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
PCA reduces the dimensionality of a 50-variable dataset to two principal components while preserving the variance, making clusters visible in a scatter plot. Parallel coordinates (C) is useful for visualizing high-dimensional data but does not inherently preserve cluster structure. Heatmap of correlations (A) shows relationships between variables, not observations. Scatter matrix (B) becomes impractical with 50 variables due to the number of plots, and it does not reduce dimensionality.
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
Heatmaps show correlations, not clustering of observations.
- ✗
Scatter matrix (pairplot)
Why it's wrong here
A scatter matrix is useful for a small number of variables, not 50.
- ✗
Parallel coordinates plot
Why it's wrong here
Parallel coordinates can show many dimensions but become cluttered and do not reduce dimensionality.
- ✓
Scatter plot of first two principal components
Why this is correct
PCA reduces dimensions while preserving variance, enabling visualization of clusters.
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