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).
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why wrong: t-SNE preserves local structure, not global.
Locally Linear Embedding (LLE)
Why wrong: LLE preserves local neighborhoods.
Principal Component Analysis (PCA)
PCA preserves global variance (covariance structure).
Uniform Manifold Approximation and Projection (UMAP)
Why wrong: UMAP focuses on local structure.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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).
Related concept
Read the scenario before looking for a memorised answer.
Uniform Manifold Approximation and Projection (UMAP)
Why it's wrong here
UMAP focuses on local structure.
Common exam traps
Cisco 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.'
Detailed technical explanation
PCA works by computing the eigenvectors of the covariance matrix of the data, projecting onto the top-k eigenvectors to maximize retained variance; this linear projection ensures that global Euclidean distances are preserved in the sense of the Frobenius norm. In contrast, t-SNE uses a Student-t distribution to model pairwise similarities in the low-dimensional space, which can cause non-convex optimization and sensitivity to perplexity, often leading to misleading global interpretations. A real-world scenario where PCA is preferred is in exploratory analysis of genomic data (e.g., 500 gene expression features) where the goal is to identify population structure (e.g., ancestry clusters) without introducing artifacts from local neighborhood preservation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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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?
mediumWhy 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.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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