The answer is to calculate mutual information scores between each feature and the target. Mutual information is the most suitable technique for identifying non-linear feature-target relationships because it quantifies the amount of information shared between two variables without assuming any functional form, capturing any kind of dependency—linear or non-linear. In contrast, Pearson correlation only measures linear relationships, while PCA is for dimensionality reduction and correlation matrices assess pairwise feature relationships, not feature-to-target links. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between statistical methods for exploratory data analysis, with a common trap being the overuse of Pearson correlation when non-linear patterns exist. Remember: if the relationship might bend, mutual information is your friend.
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 analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Calculate mutual information scores between each feature and the target.
Option D is correct because mutual information captures any kind of dependency, including non-linear. Option A is wrong because Pearson correlation only measures linear relationships. Option B is wrong because PCA is for dimensionality reduction, not feature-target relationship. Option C is wrong because correlation matrix is pairwise among features, not with 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.
✗
Visualize the correlation matrix heatmap of all features.
Why it's wrong here
Heatmap shows feature-feature correlations, not with target.
✗
Apply Principal Component Analysis (PCA) and examine the loadings.
Why it's wrong here
PCA is unsupervised; loadings don't relate to target.
✓
Calculate mutual information scores between each feature and the target.
Why this is correct
Mutual information captures non-linear dependencies.
Related concept
Read the scenario before looking for a memorised answer.
✗
Compute Pearson correlation coefficients between each feature and the target.
Why it's wrong here
Pearson only detects linear relationships.
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.
Trap categories for this question
Command / output trap
Heatmap shows feature-feature correlations, not with target.
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: Calculate mutual information scores between each feature and the target. — Option D is correct because mutual information captures any kind of dependency, including non-linear. Option A is wrong because Pearson correlation only measures linear relationships. Option B is wrong because PCA is for dimensionality reduction, not feature-target relationship. Option C is wrong because correlation matrix is pairwise among features, not with target.
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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