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 exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove one of the two highly correlated features from the dataset.
Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.
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.
✗
Create an interaction term between the two features.
Why it's wrong here
Interaction terms can increase multicollinearity and complexity.
✓
Remove one of the two highly correlated features from the dataset.
Why this is correct
Removing one feature eliminates multicollinearity, simplifying the model and improving interpretability.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the regularization parameter (e.g., lambda) in the model.
Why it's wrong here
Regularization helps but does not directly address the redundancy; correlated features can still cause instability.
✗
Apply mean-centering to both features to reduce correlation.
Why it's wrong here
Mean-centering does not change the correlation coefficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that regularization alone fixes multicollinearity, but regularization only penalizes coefficient magnitude, not the linear dependency between features.
Detailed technical explanation
How to think about this question
Multicollinearity is detected via correlation coefficients or Variance Inflation Factor (VIF); a VIF above 10 (or 5 in strict settings) indicates problematic collinearity. In linear models, near-perfect correlation makes the design matrix nearly singular, causing the inverse of X^T X to be unstable, which inflates standard errors and can lead to incorrect feature importance rankings. In tree-based models, high correlation does not harm prediction as severely, but it can still mislead feature importance metrics like gain-based importance.
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
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
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: Remove one of the two highly correlated features from the dataset. — Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.
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.
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Question Discussion
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