The answer is scatter plots with LOESS smoothing, polynomial features, and decision trees. These three techniques are correct because they directly address the challenge of exploring non-linear relationships: LOESS provides a flexible, locally weighted regression curve that visually reveals curvature in the data, polynomial features transform the predictor into higher-degree terms so that linear models like linear regression can fit bends and twists, and decision trees inherently partition the feature space into regions, capturing complex non-linear interactions without requiring manual feature engineering. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between tools that handle non-linearity versus those that assume linearity—a common trap is choosing correlation (which only measures linear association) or logistic regression (which models binary outcomes, not continuous targets). Remember the mnemonic "LPD" for LOESS, Polynomials, and Decision trees to quickly recall the three correct techniques when you see non-linear relationships on the exam.
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 data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add polynomial features (e.g., x^2, x^3) and check if model performance improves.
Options A, B, and C are correct. Scatter plots with a LOESS curve visually reveal non-linearity. Polynomial features allow linear models to capture non-linear relationships. Decision trees can model non-linear interactions without explicit feature engineering. Option D is wrong because correlation measures only linear relationships. Option E is wrong because logistic regression is for binary outcomes.
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 logistic regression to binarize the target.
Why it's wrong here
Logistic regression is for classification, not for exploring non-linearity with continuous target.
✗
Compute the Pearson correlation coefficient between the predictor and target.
Why it's wrong here
Pearson correlation only measures linear relationships.
✓
Add polynomial features (e.g., x^2, x^3) and check if model performance improves.
Why this is correct
Polynomial features capture non-linearity in linear models.
Related concept
Read the scenario before looking for a memorised answer.
✓
Fit a decision tree regressor and examine feature importance.
Why this is correct
Decision trees model non-linear relationships.
Related concept
Read the scenario before looking for a memorised answer.
✓
Create a scatter plot and overlay a LOESS (local regression) smooth curve.
Why this is correct
LOESS visually shows non-linear patterns.
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
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.
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: Add polynomial features (e.g., x^2, x^3) and check if model performance improves. — Options A, B, and C are correct. Scatter plots with a LOESS curve visually reveal non-linearity. Polynomial features allow linear models to capture non-linear relationships. Decision trees can model non-linear interactions without explicit feature engineering. Option D is wrong because correlation measures only linear relationships. Option E is wrong because logistic regression is for binary outcomes.
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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