Quick Answer
The answer is SMOTE, or Synthetic Minority Over-sampling Technique, because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This directly addresses the severe 95:5 class imbalance without losing data—which undersampling would do—and avoids the overfitting risk of naive random oversampling. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of class imbalance handling techniques in the context of data preparation for binary classification, often appearing as a scenario where you must choose between SMOTE, undersampling, and oversampling. A common trap is selecting random oversampling because it seems simpler, but SMOTE’s synthetic generation creates more robust decision boundaries for the minority class. Memory tip: SMOTE “smoothes” the imbalance by creating new, realistic minority points, not just copying old ones.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?
SMOTE (Synthetic Minority Over-sampling Technique)
Generates synthetic samples for the minority class.
Random undersampling of the majority class
Why wrong: Removes potentially valuable data.
Random oversampling of the minority class
Why wrong: Duplicates existing samples, risk of overfitting.
Apply class weights during model training
Why wrong: Affects loss function, not data preparation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.
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.
SMOTE (Synthetic Minority Over-sampling Technique)
Why this is correct
Generates synthetic samples for the minority class.
Related concept
Read the scenario before looking for a memorised answer.
Random undersampling of the majority class
Why it's wrong here
Removes potentially valuable data.
Random oversampling of the minority class
Why it's wrong here
Duplicates existing samples, risk of overfitting.
Apply class weights during model training
Why it's wrong here
Affects loss function, not data preparation.
Common exam traps
AWS often tests the distinction between data-level techniques (like SMOTE, oversampling, undersampling) and algorithm-level techniques (like class weights), and the trap here is that candidates confuse class weighting as a data preparation method when it is actually a model training adjustment, not a data transformation step.
Detailed technical explanation
SMOTE works by selecting a minority class instance, finding its k-nearest neighbors (typically k=5), and creating a synthetic sample along the line segment connecting the instance to a randomly chosen neighbor. This interpolation occurs in feature space, so the synthetic points are plausible but not exact copies, which helps the model generalize better. In real-world scenarios like fraud detection or rare disease diagnosis, SMOTE can be combined with undersampling (e.g., SMOTEENN) to clean noisy majority examples, but pure SMOTE is preferred when data loss is unacceptable.
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.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: SMOTE (Synthetic Minority Over-sampling Technique) — SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)
mediumWhy D: Option D is correct because undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.
Variation 2. A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the imbalance before training?
easyWhy D: Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.
Variation 3. A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?
easyWhy B: Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.
Variation 4. A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?
easyWhy D: SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.
Variation 5. A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?
hardWhy B: Option B is correct because SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.
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Last reviewed: Jun 30, 2026
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