Quick Answer
The answer is to check linear relationships between features and the target variable. This is the correct choice because linear regression assumes a linear relationship between each predictor and the continuous outcome, while tree-based models like random forests or gradient boosting can capture non-linear patterns without such assumptions. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of exploratory data analysis (EDA) for model selection, specifically how linearity checks guide the choice between parametric and non-parametric algorithms. A common trap is confusing multicollinearity or outlier detection as the primary factor, but those affect model stability rather than the fundamental structural assumption. Remember the memory tip: “Linearity first, then complexity” — if scatter plots show clear linear trends, start with linear regression; if not, tree models are safer.
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 Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Compute variance inflation factor (VIF) for features
Why wrong: Why B is wrong
Check linear relationships between features and target
Why A is correct
Detect outliers using Z-score
Why wrong: Why D is wrong
Identify class imbalance in the target
Why wrong: Why C is wrong
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Check linear relationships between features and target
Option A is correct because checking linearity (e.g., scatter plots of features vs. target) is fundamental for linear model assumptions. Option B is wrong because multicollinearity affects linear regression but not tree models. Option C is wrong because class imbalance is for classification. Option D is wrong because outlier detection is important but not the primary factor for model selection.
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.
Compute variance inflation factor (VIF) for features
Why it's wrong here
Why B is wrong
Check linear relationships between features and target
Why this is correct
Why A is correct
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Detect outliers using Z-score
Why it's wrong here
Why D is wrong
Identify class imbalance in the target
Why it's wrong here
Why C is wrong
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
Learn the concepts, then practise the questions
Exploratory Data Analysis practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Check linear relationships between features and target — Option A is correct because checking linearity (e.g., scatter plots of features vs. target) is fundamental for linear model assumptions. Option B is wrong because multicollinearity affects linear regression but not tree models. Option C is wrong because class imbalance is for classification. Option D is wrong because outlier detection is important but not the primary factor for model selection.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.