The correct techniques are SMOTE and class weights, as both directly address the imbalance in your training data. SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic examples for the minority class rather than simply duplicating existing ones, which helps the model learn more robust decision boundaries. Class weights, on the other hand, adjust the loss function to assign a higher penalty to misclassifications of the minority class, forcing the model to pay more attention to the rare churn cases. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical data imbalance strategies beyond simple resampling—a common trap is choosing downsampling the majority class, which discards potentially valuable data, or relying on accuracy as a metric, which is misleading for imbalanced datasets. A useful memory tip: think of SMOTE as “synthesizing” new minority data and class weights as “weighting” the error—both boost the minority signal without losing majority information.
MLS-C01 Modeling Practice Question
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 data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Synthetic Minority Over-sampling Technique (SMOTE)
Correct options: B (Synthetic Minority Over-sampling Technique) and C (Use class weights in the loss function). SMOTE generates synthetic samples for the minority class, and class weights penalize misclassifications of the minority class more heavily. Option A (Downsampling the majority class) can be used but may discard data; not as effective as SMOTE. Option D (Use accuracy as evaluation metric) is misleading. Option E (Increase model complexity) may cause overfitting.
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.
✗
Downsample the majority class to balance the dataset
Why it's wrong here
Downsampling discards data, which can be suboptimal.
✓
Use Synthetic Minority Over-sampling Technique (SMOTE)
Why this is correct
SMOTE generates synthetic samples for the minority class.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use class weights in the loss function to penalize misclassifications of the minority class
Why this is correct
Class weights focus the model on the minority class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
✗
Increase the model complexity by adding more layers
Why it's wrong here
Increased complexity may lead to overfitting.
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 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 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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Synthetic Minority Over-sampling Technique (SMOTE) — Correct options: B (Synthetic Minority Over-sampling Technique) and C (Use class weights in the loss function). SMOTE generates synthetic samples for the minority class, and class weights penalize misclassifications of the minority class more heavily. Option A (Downsampling the majority class) can be used but may discard data; not as effective as SMOTE. Option D (Use accuracy as evaluation metric) is misleading. Option E (Increase model complexity) may cause overfitting.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). Which TWO techniques should be used to address the class imbalance? (Choose 2.)
easy
A.Use a higher learning rate during training
B.Use L1 regularization on the model
✓ C.Use random undersampling of the majority class
✓ D.Use SMOTE to generate synthetic samples for the minority class
E.Use principal component analysis (PCA) to reduce dimensionality
Why C: SMOTE and undersampling are standard techniques for handling class imbalance.
Variation 2. A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?
hard
A.Undersample the majority class
B.Use RMSE as the evaluation metric
✓ C.Oversample the minority class using SMOTE
D.Use accuracy as the evaluation metric
✓ E.Use class weights in the loss function
Why C: Oversampling the minority class using SMOTE (Synthetic Minority Oversampling Technique) is correct because it generates synthetic samples for the minority class by interpolating between existing minority instances, rather than simply duplicating them. This helps balance the dataset without introducing exact copies, which can reduce overfitting and improve the model's ability to generalize to the minority class.
Last reviewed: Jun 20, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.