Quick Answer
The correct answer is SMOTE, or Synthetic Minority Oversampling Technique, because it generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset without discarding valuable majority-class data. This approach is the most effective for handling class imbalance on Amazon SageMaker, as it directly improves model recall for the churn class while avoiding the information loss caused by undersampling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing for imbalanced binary classification—a common scenario in churn or fraud detection questions. A frequent trap is choosing random oversampling (which duplicates existing points and risks overfitting) or undersampling (which discards majority data). Remember the mnemonic: “SMOTE adds notes—synthetic, not copies—to balance the classes without losing faces.”
This MLS-C01 practice question tests your understanding of modeling. 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 company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?
Use accuracy as the evaluation metric.
Why wrong: Accuracy is not suitable for imbalanced datasets.
Undersample the majority class.
Why wrong: Undersampling may discard valuable data.
Use SMOTE to generate synthetic samples for the minority class.
SMOTE is a standard oversampling technique.
Train a random forest model instead of logistic regression.
Why wrong: Algorithm change does not address 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
Use SMOTE to generate synthetic samples for the minority class.
SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.
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.
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy is not suitable for imbalanced datasets.
Undersample the majority class.
Why it's wrong here
Undersampling may discard valuable data.
Use SMOTE to generate synthetic samples for the minority class.
Why this is correct
SMOTE is a standard oversampling technique.
Related concept
Read the scenario before looking for a memorised answer.
Train a random forest model instead of logistic regression.
Why it's wrong here
Algorithm change does not address imbalance.
Common exam traps
The trap here is that candidates often assume switching to a tree-based model (like random forest) inherently solves class imbalance, but the exam tests that explicit resampling or cost-sensitive techniques are required for effective handling.
Detailed technical explanation
SMOTE works by selecting a minority-class sample, finding its k-nearest neighbors (typically k=5), and creating synthetic points along the line segments connecting the sample to its neighbors. In SageMaker, you can implement SMOTE using the built-in SMOTE algorithm in the Data Wrangler or via a custom preprocessing script in a training pipeline, ensuring the synthetic data is generated only on the training set to avoid data leakage. A real-world scenario where SMOTE excels is when churn events are rare but costly, such as in telecom or subscription services, where improving recall for the minority class directly impacts retention strategies.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use SMOTE to generate synthetic samples for the minority class. — SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.
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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Last reviewed: Jun 24, 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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