Quick Answer
The answer is a scree plot of principal components, because it directly visualizes the variance explained by each principal component derived from PCA, allowing you to identify the "elbow" where additional components contribute diminishing returns. This technique is essential for understanding how much of the dataset’s total variance is captured by each feature’s linear combination, guiding your dimensionality reduction decision. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to choose the right EDA tool for feature reduction—a common trap is confusing a scree plot with a correlation heatmap, which shows pairwise relationships but not variance. Remember, a scree plot answers "how much variance does each component explain?" while a heatmap answers "how do features relate to each other?" For the exam, think: "Scree for variance, heatmap for pairs."
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 uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?
Histogram of the target variable
Why wrong: Histogram shows target distribution, not feature variance.
Scree plot of principal components
Scree plot displays variance explained by each component.
Heatmap of feature correlations
Why wrong: Heatmap shows correlations, not variance explained.
Box plot of each feature
Why wrong: Box plots show quartiles and outliers, not variance explained.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Scree plot of principal components
A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option C is wrong because a histogram shows distribution. Option D is wrong because a box plot shows summary statistics.
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.
Histogram of the target variable
Why it's wrong here
Histogram shows target distribution, not feature variance.
Scree plot of principal components
Why this is correct
Scree plot displays variance explained by each component.
Related concept
Read the scenario before looking for a memorised answer.
Heatmap of feature correlations
Why it's wrong here
Heatmap shows correlations, not variance explained.
Box plot of each feature
Why it's wrong here
Box plots show quartiles and outliers, not variance explained.
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.
Trap categories for this question
Command / output trap
Histogram shows target distribution, not feature variance.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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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: Scree plot of principal components — A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option C is wrong because a histogram shows distribution. Option D is wrong because a box plot shows summary statistics.
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.
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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