Question 3 of 1,672
PCA for Global Structure Preservation in Visualization
A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?
Quick Answer
The answer is Principal Component Analysis (PCA). PCA is the correct choice for preserving global data structure in visualization because it is a linear dimensionality reduction technique that maximizes variance along orthogonal principal components, effectively capturing the overall covariance structure of the 500 features. This ensures that global relationships—such as the distances between clusters or broad data patterns—are retained, unlike nonlinear methods like t-SNE or UMAP, which prioritize local neighborhood structure and can distort global distances. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of when to apply linear versus nonlinear techniques for dimensionality reduction; a common trap is choosing t-SNE for visualization without recognizing that it sacrifices global structure for local detail. A helpful memory tip: PCA is for the “big picture” (global variance), while t-SNE is for the “neighborhood watch” (local clusters).
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that nonlinear methods like t-SNE or UMAP are always better for visualization, but the trap here is that they sacrifice global structure for local detail, making PCA the correct choice when the question explicitly states 'preserve global data 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
✓
Principal Component Analysis (PCA)
PCA is the most suitable technique for preserving the global data structure when reducing dimensionality because it is a linear method that maximizes variance along orthogonal principal components, capturing the overall covariance structure of the 500 features. Unlike nonlinear methods, PCA ensures that the global relationships (e.g., distances between clusters) are retained, making it ideal for visualization of high-dimensional data where the goal is to see broad patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
t-SNE preserves local structure, not global.
- ✗
Locally Linear Embedding (LLE)
Why it's wrong here
LLE preserves local neighborhoods.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA preserves global variance (covariance structure).
- ✗
Uniform Manifold Approximation and Projection (UMAP)
Why it's wrong here
UMAP focuses on local structure.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer is exploring a dataset with 50 features. Some features are highly correlated. Which technique should the engineer use to reduce dimensionality while preserving variance?
medium- ✓ A.Principal Component Analysis (PCA)
- B.Factor Analysis
- C.t-Distributed Stochastic Neighbor Embedding (t-SNE)
- D.Linear Discriminant Analysis (LDA)
Why A: PCA (Principal Component Analysis) is the standard technique for dimensionality reduction by projecting data onto principal components that capture maximum variance. LDA is supervised and aims to separate classes. t-SNE is for visualization. Autoencoders can reduce dimensionality but are more complex. Factor analysis assumes latent factors.
Last reviewed: Jun 24, 2026
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