The correct action is to remove one feature from each highly correlated pair based on domain knowledge or higher correlation with the target. This directly addresses handling multicollinearity in feature selection, because when features exhibit correlation above 0.95, they introduce redundancy that destabilizes coefficient estimates in linear models and inflates variance, making the model less reliable and harder to interpret. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of practical EDA workflows—specifically that removing redundant features is preferred over dimensionality reduction techniques like PCA when interpretability and alignment with a binary target matter. A common trap is to immediately apply PCA, but that transforms features into uninterpretable components and ignores the target variable’s relationship. Memory tip: “Correlated pairs? Cut the weaker link—keep the one that knows the target best.”
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 scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target.
Option C is correct because when features are highly correlated (e.g., > 0.95), they introduce multicollinearity, which can destabilize coefficient estimates in linear models and reduce interpretability. Removing one feature from each correlated pair based on domain knowledge or its correlation with the target variable preserves predictive power while reducing redundancy. This approach is more targeted than PCA, which transforms features into uncorrelated components but sacrifices interpretability and may not align with the binary target.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Apply PCA to all features to decorrelate them.
Why it's wrong here
PCA reduces dimensionality but loses interpretability and may not be needed.
✗
Standardize all features using StandardScaler.
Why it's wrong here
Scaling does not reduce multicollinearity.
✓
For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target.
Why this is correct
This reduces redundancy while retaining predictive power.
Related concept
Read the scenario before looking for a memorised answer.
✗
Randomly drop half of the correlated features.
Why it's wrong here
Random dropping may remove important features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that PCA is the default solution for multicollinearity, but the trap here is that PCA transforms features into uninterpretable components, whereas removing correlated features directly preserves the original feature space and domain relevance.
Detailed technical explanation
How to think about this question
Multicollinearity inflates the variance of coefficient estimates in linear models (e.g., logistic regression), making them unstable and hard to interpret. In practice, a correlation threshold of 0.95 is aggressive; many practitioners use 0.8 or 0.9, and the Variance Inflation Factor (VIF) is a more robust metric, where VIF > 10 indicates severe multicollinearity. For tree-based models like Random Forest, multicollinearity is less problematic, but for linear models or when feature importance is needed, removing correlated features is critical.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target. — Option C is correct because when features are highly correlated (e.g., > 0.95), they introduce multicollinearity, which can destabilize coefficient estimates in linear models and reduce interpretability. Removing one feature from each correlated pair based on domain knowledge or its correlation with the target variable preserves predictive power while reducing redundancy. This approach is more targeted than PCA, which transforms features into uncorrelated components but sacrifices interpretability and may not align with the binary target.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?
medium
A.Use t-SNE to visualize feature relationships
B.Apply PCA and examine the loadings
C.Compute mutual information between each feature and the target
D.Use chi-square test for each pair
✓ E.Create a correlation matrix and visualize with a heatmap
Why E: A correlation matrix with a heatmap visualizes pairwise correlations and helps identify groups of correlated features. Option B is wrong because PCA reduces dimensionality but does not show feature groups directly. Option C is wrong because mutual information measures dependency but not specifically linear correlation. Option D is wrong because chi-square test is for categorical associations. Option E is wrong because t-SNE is for visualization of high-dimensional data, not for correlation analysis.
Variation 2. A data scientist is performing EDA on a dataset with 100 features. They want to reduce dimensionality by removing highly correlated features. Which TWO approaches are appropriate? (Choose TWO.)
medium
A.Use feature importance from a random forest to select top features.
B.Remove features with low variance using VarianceThreshold.
✓ C.Compute a correlation matrix and remove one feature from each pair with correlation >0.95.
✓ D.Use Principal Component Analysis (PCA) and select components that explain 95% of variance.
E.Apply L1 regularization (Lasso) during model training to zero out coefficients of correlated features.
Why C: Options A and D are correct. Option A: Removing features with correlation >0.95 directly reduces redundancy. Option D: Using PCA creates uncorrelated components. Option B is wrong because L1 regularization is a modeling technique, not EDA. Option C is wrong because feature importance from tree-based models is not specifically for removing correlated features.
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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.
Question Discussion
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