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Exploratory Data Analysis →easyMultiple ChoiceObjective-mapped

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 uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?

A
Synthetic Minority Oversampling Technique (SMOTE)
SMOTE creates synthetic minority samples to balance the dataset.
B
One-hot encoding of the target variable
Why wrong: Encoding the target is not applicable for balancing.
C
Random undersampling of the majority class
Why wrong: Undersampling loses data and may discard useful information.
D
Min-Max scaling of all features
Why wrong: Scaling does not address class imbalance.

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

✓

Synthetic Minority Oversampling Technique (SMOTE)

Synthetic Minority Oversampling Technique (SMOTE) is the correct technique because it generates synthetic samples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, effectively balancing the dataset without simply duplicating data. Amazon SageMaker Data Wrangler includes a built-in SMOTE transform, making it directly applicable for handling imbalanced target variables during exploratory data analysis.

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.

✓
Synthetic Minority Oversampling Technique (SMOTE)
Why this is correct
SMOTE creates synthetic minority samples to balance the dataset.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encoding of the target variable
Why it's wrong here
Encoding the target is not applicable for balancing.
✗
Random undersampling of the majority class
Why it's wrong here
Undersampling loses data and may discard useful information.
✗
Min-Max scaling of all features
Why it's wrong here
Scaling does not address class imbalance.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The MLS-C01 exam often tests the misconception that random undersampling is always safe, but the trap here is that candidates may overlook the information loss from discarding majority class data, while SMOTE provides a more robust synthetic oversampling approach.

Detailed technical explanation

How to think about this question

SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and generating a new synthetic sample along the line segment connecting the sample to a randomly chosen neighbor. In SageMaker Data Wrangler, SMOTE is implemented as a transform that can be applied directly to a flow, and it is particularly effective when the minority class is not too sparse, as it avoids the overfitting risk of simple oversampling.

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.

TExam Day Tips

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 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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: Synthetic Minority Oversampling Technique (SMOTE) — Synthetic Minority Oversampling Technique (SMOTE) is the correct technique because it generates synthetic samples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, effectively balancing the dataset without simply duplicating data. Amazon SageMaker Data Wrangler includes a built-in SMOTE transform, making it directly applicable for handling imbalanced target variables during exploratory data analysis.

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

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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.