The answer is Principal Component Analysis (PCA). PCA is appropriate because it is a linear dimensionality reduction technique that transforms a high-dimensional dataset with 500 features into a smaller set of uncorrelated principal components while retaining maximum variance, making it ideal for exploratory data analysis (EDA) before modeling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between feature selection methods like the Chi-square test and actual dimensionality reduction techniques; a common trap is confusing cross-validation or one-hot encoding with reduction. Remember that PCA and t-SNE are both dimensionality reduction techniques, but PCA is linear and t-SNE is nonlinear—for EDA on tabular data with many features, PCA is the go-to choice. A helpful memory tip: PCA “projects” data onto new axes, while Chi-square “selects” existing features.
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 EDA on a tabular dataset with 500 features. The goal is to reduce dimensionality before modeling. Which TWO techniques are appropriate for this task?
PCA and t-SNE are both dimensionality reduction techniques. PCA is linear, t-SNE is nonlinear. Option A (Chi-square test) is for feature selection with categorical targets, not dimensionality reduction. Option C (cross-validation) is for model evaluation. Option E (one-hot encoding) expands features.
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-fold cross-validation to assess model performance.
Why it's wrong here
Model validation technique.
✗
Chi-square test for independence between features.
Why it's wrong here
Feature selection, not dimensionality reduction.
✗
One-hot encoding of categorical variables.
Why it's wrong here
Increases dimensionality.
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: t-distributed Stochastic Neighbor Embedding (t-SNE). — PCA and t-SNE are both dimensionality reduction techniques. PCA is linear, t-SNE is nonlinear. Option A (Chi-square test) is for feature selection with categorical targets, not dimensionality reduction. Option C (cross-validation) is for model evaluation. Option E (one-hot encoding) expands features.
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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