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Question 199 of 1,755

Exploratory Data Analysis →hardMultiple SelectObjective-mapped

Quick Answer

The answer is to calculate mutual information between each feature and the target, keep top k features, along with correlation-based feature selection and variance threshold. These three methods are suitable for feature selection in high-dimensional data (p >> n) because they directly filter features based on their statistical relationship to the target or their own variance, without relying on the model itself. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between true feature selection techniques and dimensionality reduction or embedded methods—a common trap is confusing PCA or Lasso with filter methods used during exploratory data analysis. Remember that PCA transforms features rather than selecting them, and Lasso is a modeling technique, not an EDA tool. For a quick memory tip: think of the three Fs—Filter by correlation, Filter by mutual information, and Filter by variance—to avoid the trap of picking PCA or Lasso.

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 machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)

Question 1hardmulti select

Full question →

A
Fit a Lasso regression model and select features with non-zero coefficients
Why wrong: Lasso is a modeling step, not exploratory; it may not be suitable for all data types.
B
Remove features with variance below a threshold (e.g., <0.01)
Low-variance features provide little information and can be removed.
C
Remove features with high pairwise correlation (e.g., >0.95)
High correlation indicates redundancy; removing them reduces multicollinearity.
D
Calculate mutual information between each feature and the target, and keep top k features
Mutual information captures non-linear dependencies and is useful for feature selection.
E
Apply Principal Component Analysis (PCA) and select top components
Why wrong: PCA creates new features, it does not select original features.

Full breakdown with real-world context →

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

✓

Remove features with variance below a threshold (e.g., <0.01)

Option A is correct because correlation-based feature selection removes highly correlated features. Option C is correct because mutual information measures relevance to the target. Option E is correct because Variance Threshold removes low-variance features. Option B is wrong because PCA is a dimensionality reduction technique, not a feature selection method. Option D is wrong because Lasso regression is a modeling technique, not typically used in EDA.

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.

✗
Fit a Lasso regression model and select features with non-zero coefficients
Why it's wrong here
Lasso is a modeling step, not exploratory; it may not be suitable for all data types.
✓
Remove features with variance below a threshold (e.g., <0.01)
Why this is correct
Low-variance features provide little information and can be removed.
Related concept
Read the scenario before looking for a memorised answer.
✓
Remove features with high pairwise correlation (e.g., >0.95)
Why this is correct
High correlation indicates redundancy; removing them reduces multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✓
Calculate mutual information between each feature and the target, and keep top k features
Why this is correct
Mutual information captures non-linear dependencies and is useful for feature selection.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply Principal Component Analysis (PCA) and select top components
Why it's wrong here
PCA creates new features, it does not select original features.

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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

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: Remove features with variance below a threshold (e.g., <0.01) — Option A is correct because correlation-based feature selection removes highly correlated features. Option C is correct because mutual information measures relevance to the target. Option E is correct because Variance Threshold removes low-variance features. Option B is wrong because PCA is a dimensionality reduction technique, not a feature selection method. Option D is wrong because Lasso regression is a modeling technique, not typically used in EDA.

What should I do if I get this MLS-C01 question wrong?

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.