Quick Answer
The answer is Synthetic Minority Oversampling Technique (SMOTE). This is the correct choice because SMOTE addresses class imbalance by generating synthetic samples for the minority class rather than simply duplicating existing ones, which helps the model learn more robust decision boundaries without introducing exact replication bias. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of data preprocessing techniques for imbalanced datasets, a common scenario in real-world ML pipelines like fraud detection or rare event prediction. A frequent trap is confusing SMOTE with random undersampling, which discards majority class data and risks losing valuable information, or with scaling or encoding, which do not affect class distribution at all. Remember the memory tip: SMOTE “smooths over” the minority gap by creating new, plausible neighbors, not just copying old ones.
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?
Synthetic Minority Oversampling Technique (SMOTE)
SMOTE creates synthetic minority samples to balance the dataset.
One-hot encoding of the target variable
Why wrong: Encoding the target is not applicable for balancing.
Random undersampling of the majority class
Why wrong: Undersampling loses data and may discard useful information.
Min-Max scaling of all features
Why wrong: Scaling does not address class imbalance.
Answer choices
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)
Option D is correct because SMOTE generates synthetic samples for the minority class. Option A is wrong because it discards majority class data. Option B is wrong because scaling does not balance classes. Option C is wrong because encoding is for categorical features.
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.
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
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
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: Synthetic Minority Oversampling Technique (SMOTE) — Option D is correct because SMOTE generates synthetic samples for the minority class. Option A is wrong because it discards majority class data. Option B is wrong because scaling does not balance classes. Option C is wrong because encoding is for categorical features.
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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