The answer is to use correlation analysis or mutual information for feature selection to improve logistic regression accuracy. This is correct because logistic regression with many irrelevant features can overfit noise, leading to low test accuracy despite many non-zero coefficients; selecting the most relevant features reduces dimensionality, minimizes overfitting, and enhances generalization. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that feature selection is a critical EDA step before modeling, especially when dealing with high-dimensional data and a binary target. A common trap is confusing feature scaling (which doesn’t remove features) or PCA (which reduces dimensions but sacrifices interpretability) with direct feature selection. Remember the mnemonic: “Many non-zero coefficients? Cut the noise with correlation or mutual information choice.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next 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
✓
Use correlation analysis or mutual information to select the most relevant features.
Option B is correct because feature selection helps reduce noise and overfitting, improving model accuracy. Option A is wrong because scaling does not reduce the number of features. Option C is wrong because PCA may lose interpretability and is not directly aimed at reducing overfitting due to irrelevant features. Option D is wrong because more data does not necessarily address the issue of irrelevant features.
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 Principal Component Analysis (PCA) to reduce dimensionality.
Why it's wrong here
PCA creates new features that may be less interpretable and does not directly address overfitting from irrelevant features.
✗
Collect more training data to improve generalization.
Why it's wrong here
Adding data may not help if the model is overfitting to irrelevant features.
✗
Normalize the features using StandardScaler.
Why it's wrong here
Scaling does not reduce the number of features or address irrelevant features.
✓
Use correlation analysis or mutual information to select the most relevant features.
Why this is correct
Feature selection removes irrelevant features, reducing noise and overfitting.
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: Use correlation analysis or mutual information to select the most relevant features. — Option B is correct because feature selection helps reduce noise and overfitting, improving model accuracy. Option A is wrong because scaling does not reduce the number of features. Option C is wrong because PCA may lose interpretability and is not directly aimed at reducing overfitting due to irrelevant features. Option D is wrong because more data does not necessarily address the issue of irrelevant features.
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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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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