Principal Component Analysis (PCA) is the correct technique to identify which features contribute most to the variance in a dataset. PCA works by transforming the original features into a set of orthogonal principal components, each capturing the maximum possible remaining variance, and the loadings (coefficients) of each original feature on these components directly reveal which features drive the variance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of unsupervised dimensionality reduction versus other algorithms—a common trap is confusing PCA with t-SNE, which is for visualization only, or LDA, which requires labeled data. Remember that PCA is variance-focused and unsupervised, making it ideal for exploratory analysis without target labels. A quick memory tip: PCA = Principal Components of Variance, so when you see “variance contribution,” think PCA first.
MLS-C01 Exploratory Data Analysis Practice Question
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 data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?
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 dimensionality reduction technique that identifies the directions (principal components) that maximize variance. Option B is wrong because t-SNE is for visualization and does not provide variance contributions. Option C is wrong because LDA is supervised and requires labels. Option D is wrong because K-means is clustering, not variance analysis.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
K-means clustering
Why it's wrong here
K-means is used for clustering, not variance analysis.
✓
Principal Component Analysis (PCA)
Why this is correct
PCA decomposes the data into components that capture the maximum variance.
t-SNE is for visualization of high-dimensional data, not for variance decomposition.
✗
Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA is a supervised technique that maximizes class separability.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
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
How to think about this question
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.
KKey Concepts to Remember
OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.
TExam Day Tips
→Check area mismatch first when OSPF adjacency fails.
→Review passive interfaces when a network is advertised but no neighbour forms.
→Use show ip ospf neighbor and show ip route clues carefully.
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
How this comes up in practice
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
Got this wrong? Here's your next step.
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 — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Principal Component Analysis (PCA) — Option A is correct because PCA is a dimensionality reduction technique that identifies the directions (principal components) that maximize variance. Option B is wrong because t-SNE is for visualization and does not provide variance contributions. Option C is wrong because LDA is supervised and requires labels. Option D is wrong because K-means is clustering, not variance analysis.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Question Discussion
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