The correct answer is to remove the feature from the dataset. A feature with a correlation of 0.95 to the target is a classic red flag for data leakage from high correlation feature, meaning it likely contains information that would not be available at inference time, such as a direct derivative of the target variable. Keeping it would cause the model to overfit by learning this leaked relationship rather than generalizable patterns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to spot target leakage before modeling, often disguised as an “ideal” feature in exploratory analysis. A common trap is assuming regularization or PCA can fix it, but neither removes the underlying leak—PCA may even preserve it in a principal component. Remember the memory tip: if correlation is near 1.0, it’s probably a leak, not a gem.
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 using Amazon SageMaker Data Wrangler to explore a dataset. They notice that a feature has a very high correlation (0.95) with the target variable. What should they do to avoid overfitting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the feature from the dataset
Option A is correct because a feature with correlation 0.95 may be directly derived from the target or leaking information; removing it prevents overfitting. Option B is wrong because PCA is for dimensionality reduction but may still include the leak. Option C is wrong because regularization helps but may not fully address leakage. Option D is wrong because scaling does not address correlation.
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.
✗
Use L2 regularization in the model
Why it's wrong here
Incorrect: Regularization reduces coefficient magnitude but does not address leakage.
✗
Apply PCA to reduce dimensionality
Why it's wrong here
Incorrect: PCA may still contain the leaking information in principal components.
✗
Standardize the feature using StandardScaler
Why it's wrong here
Incorrect: Scaling does not change correlation or prevent overfitting.
✓
Remove the feature from the dataset
Why this is correct
Correct: High correlation with target can indicate data leakage; removing is safest.
Related concept
Read the scenario before looking for a memorised answer.
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
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 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.
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 the feature from the dataset — Option A is correct because a feature with correlation 0.95 may be directly derived from the target or leaking information; removing it prevents overfitting. Option B is wrong because PCA is for dimensionality reduction but may still include the leak. Option C is wrong because regularization helps but may not fully address leakage. Option D is wrong because scaling does not address correlation.
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.
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Question Discussion
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