Quick Answer
The correct answer is to use class weights to penalize misclassifications of minority classes. This technique directly addresses multiclass imbalance by assigning higher penalty coefficients to errors on underrepresented classes, forcing the model to pay more attention to them during training and improving recall without discarding valuable majority-class data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of cost-sensitive learning as a practical alternative to resampling; a common trap is confusing class weights with oversampling or dimensionality reduction. Remember that class weights modify the loss function, not the data distribution, making them efficient for large-scale multiclass problems with many minority groups. A useful memory tip: “Weight the loss, not the data” — class weights penalize mistakes on rare classes more heavily, boosting their performance without risking overfitting from duplication.
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 building a multiclass classification model using Amazon SageMaker. The dataset has 100 classes and is highly imbalanced. The model currently achieves high accuracy on the majority classes but poor performance on minority classes. Which technique should the data scientist use to improve minority class performance?
Apply random oversampling with replacement
Why wrong: Can lead to overfitting.
Apply principal component analysis (PCA)
Why wrong: PCA does not address class imbalance.
Use class weights to penalize misclassifications of minority classes
Effectively balances the loss function.
Remove samples from majority classes
Why wrong: Loses data and may harm overall performance.
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 class weights to penalize misclassifications of minority classes
Option C is correct because class weights penalize errors on minority classes more, improving their recall. Option A is wrong because removing majority classes would lose data. Option B is wrong because oversampling without replacement may cause overfitting. Option D is wrong because principal component analysis (PCA) is dimensionality reduction, not for imbalance.
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.
Apply random oversampling with replacement
Why it's wrong here
Can lead to overfitting.
Apply principal component analysis (PCA)
Why it's wrong here
PCA does not address class imbalance.
Use class weights to penalize misclassifications of minority classes
Why this is correct
Effectively balances the loss function.
Related concept
Read the scenario before looking for a memorised answer.
Remove samples from majority classes
Why it's wrong here
Loses data and may harm overall performance.
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use class weights to penalize misclassifications of minority classes — Option C is correct because class weights penalize errors on minority classes more, improving their recall. Option A is wrong because removing majority classes would lose data. Option B is wrong because oversampling without replacement may cause overfitting. Option D is wrong because principal component analysis (PCA) is dimensionality reduction, not for imbalance.
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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