Quick Answer
The answer is that features with near-zero variance should be removed. This is correct because low variance feature removal targets columns that contain almost constant values across observations, offering negligible predictive power and adding noise rather than signal. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of variance-based filtering as a dimensionality reduction technique during exploratory data analysis. A common trap is confusing high variance with irrelevance—high variance often indicates useful information, while near-zero variance is the true red flag. Remember the memory tip: “Zero variance, zero value” to quickly recall that features with little to no spread are prime candidates for removal.
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 team has a dataset with 500 features and wants to reduce dimensionality. During EDA, they compute the variance of each feature. Which finding would most likely lead to feature removal?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Some features have high correlation with each other
Why wrong: Why C is wrong
Some features have negative covariance with the target
Why wrong: Why D is wrong
Some features have very high variance
Why wrong: Why A is wrong
Some features have near-zero variance
Why B is correct
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Some features have near-zero variance
Option B is correct because near-zero variance features provide little information and can be removed. Option A is wrong because high variance is often useful. Option C is wrong because high correlation between two features might warrant removal of one, but variance is not the direct indicator. Option D is wrong because negative covariance is still informative.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Some features have high correlation with each other
Why it's wrong here
Why C is wrong
Some features have negative covariance with the target
Why it's wrong here
Why D is wrong
Some features have very high variance
Why it's wrong here
Why A is wrong
Some features have near-zero variance
Why this is correct
Why B is correct
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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 — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Some features have near-zero variance — Option B is correct because near-zero variance features provide little information and can be removed. Option A is wrong because high variance is often useful. Option C is wrong because high correlation between two features might warrant removal of one, but variance is not the direct indicator. Option D is wrong because negative covariance is still informative.
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.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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