Quick Answer
The answer is Principal Component Analysis (PCA). PCA is the most suitable technique for dimensionality reduction for visualization when preserving global structure is the priority because it is a linear method that projects data onto orthogonal axes capturing maximum variance, thereby maintaining the overall spread and relationships across all data points. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how different dimensionality reduction techniques handle structure: PCA preserves global variance, while t-SNE excels at revealing local clusters but distorts distances. A common trap is choosing t-SNE for visualization without recognizing that it sacrifices global layout for local neighborhood details. Remember the memory tip: PCA is for the “big picture” (global), t-SNE is for the “neighborhood” (local).
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 company has a dataset with 1 million rows and 500 features. They want to reduce dimensionality for visualization. Which technique is most suitable for preserving global structure?
Autoencoder
Why wrong: Autoencoders are neural networks, not primarily for visualization.
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why wrong: t-SNE preserves local structure, not global.
Linear Discriminant Analysis (LDA)
Why wrong: LDA is supervised and requires class labels.
Principal Component Analysis (PCA)
PCA preserves global variance.
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)
Option A is correct because PCA is a linear technique that preserves global variance. Option B is wrong because t-SNE focuses on local structure. Option C is wrong because LDA requires labels. Option D is wrong because Autoencoders are more complex and not primarily for visualization.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Autoencoder
Why it's wrong here
Autoencoders are neural networks, not primarily for visualization.
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
t-SNE preserves local structure, not global.
Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA is supervised and requires class labels.
Principal Component Analysis (PCA)
Why this is correct
PCA preserves global variance.
Related concept
OSPF neighbours must agree on key parameters.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
The correct answer is: Principal Component Analysis (PCA) — Option A is correct because PCA is a linear technique that preserves global variance. Option B is wrong because t-SNE focuses on local structure. Option C is wrong because LDA requires labels. Option D is wrong because Autoencoders are more complex and not primarily for visualization.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 20, 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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