- A
Collect more training data to reduce the impact of correlated features.
Why wrong: Adding more data does not reduce correlation between features.
- B
Increase the regularization parameter in the model.
Why wrong: Regularization helps but is not the most appropriate first step when many features are correlated; PCA is more comprehensive.
- C
Apply principal component analysis (PCA) to reduce dimensionality.
PCA reduces multicollinearity by transforming correlated features into orthogonal components.
- D
Remove all features with correlation above 0.95.
Why wrong: Removing all such features may discard important information; consider PCA or regularization.
Question 921 of 1,755
PCA for Highly Correlated Features | AWS Machine Learning Specialty Explained
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 analyzing a dataset with 500 features and 10,000 samples. After running a correlation matrix, they find that many feature pairs have correlation >0.95. What is the most appropriate next step to improve model performance?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply principal component analysis (PCA) to reduce dimensionality.
Option C is correct because PCA reduces dimensionality by transforming correlated features into uncorrelated principal components, addressing multicollinearity while retaining most of the variance. Option A is wrong: collecting more data does not reduce correlation between features. Option B is wrong: increasing regularization (e.g., L2) can mitigate multicollinearity effects, but with 500 features and many highly correlated pairs, PCA is more effective as a dimensionality reduction technique. Option D is wrong: removing all features with correlation >0.95 may discard useful information and is less systematic than PCA.
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.
- ✗
Collect more training data to reduce the impact of correlated features.
Why it's wrong here
Adding more data does not reduce correlation between features.
- ✗
Increase the regularization parameter in the model.
Why it's wrong here
Regularization helps but is not the most appropriate first step when many features are correlated; PCA is more comprehensive.
- ✓
Apply principal component analysis (PCA) to reduce dimensionality.
Why this is correct
PCA reduces multicollinearity by transforming correlated features into orthogonal components.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all features with correlation above 0.95.
Why it's wrong here
Removing all such features may discard important information; consider PCA or regularization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Apply principal component analysis (PCA) to reduce dimensionality. — Option C is correct because PCA reduces dimensionality by transforming correlated features into uncorrelated principal components, addressing multicollinearity while retaining most of the variance. Option A is wrong: collecting more data does not reduce correlation between features. Option B is wrong: increasing regularization (e.g., L2) can mitigate multicollinearity effects, but with 500 features and many highly correlated pairs, PCA is more effective as a dimensionality reduction technique. Option D is wrong: removing all features with correlation >0.95 may discard useful information and is less systematic than PCA.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
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 analyzing a dataset with 50 features and 10,000 samples. After generating a correlation matrix, they notice several pairs of features have correlation coefficients above 0.95. What should the data scientist do to prepare the data for linear regression?
medium- A.Apply PCA to reduce dimensionality to 10 components.
- ✓ B.Remove one feature from each highly correlated pair.
- C.Drop all features with correlation above 0.95.
- D.Standardize all features using StandardScaler.
Why B: Option B is correct because high correlation between features indicates multicollinearity, which can destabilize linear regression coefficients. Removing one feature from each highly correlated pair reduces redundancy without losing all information. Option A is wrong because applying PCA creates new components that lose the interpretability of the original features and is not a direct method to address multicollinearity in the context of preparing data for linear regression. Option C is wrong because dropping all features with correlation above 0.95 may discard useful information; it is better to remove only one feature from each correlated pair. Option D is wrong because standardizing features does not reduce multicollinearity; it only changes the scale of features.
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Last reviewed: Jun 20, 2026
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