- A
Train the model only on the majority class.
Why wrong: Ignoring minority class completely would fail to detect fraud.
- B
Use accuracy as the evaluation metric.
Why wrong: Accuracy can be high even if minority class is ignored; use precision-recall or AUC.
- C
Apply SMOTE to generate synthetic samples of the minority class.
SMOTE creates synthetic examples to balance classes.
- D
Use class weights in the loss function.
Class weights increase penalty for minority class errors.
- E
Undersample the majority class.
Reducing majority class samples can help balance the dataset.
Question 229 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 using SageMaker to build a model for fraud detection. The dataset is highly imbalanced. Which THREE techniques should be applied to address class imbalance?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply SMOTE to generate synthetic samples of the minority class.
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the dataset without simply duplicating data. This is effective for fraud detection because it provides the model with more diverse examples of fraudulent transactions, reducing the bias toward the majority class.
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.
- ✗
Train the model only on the majority class.
Why it's wrong here
Ignoring minority class completely would fail to detect fraud.
- ✗
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy can be high even if minority class is ignored; use precision-recall or AUC.
- ✓
Apply SMOTE to generate synthetic samples of the minority class.
Why this is correct
SMOTE creates synthetic examples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use class weights in the loss function.
Why this is correct
Class weights increase penalty for minority class errors.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Undersample the majority class.
Why this is correct
Reducing majority class samples can help balance the dataset.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS exams often test the misconception that accuracy is a valid metric for imbalanced data, when in fact precision, recall, F1-score, or AUC-ROC are more appropriate.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating a synthetic sample along the line segment connecting the sample to a randomly chosen neighbor. This avoids overfitting that would occur with simple oversampling and can be combined with undersampling (e.g., SMOTETomek) for even better results in highly skewed datasets like fraud detection.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply SMOTE to generate synthetic samples of the minority class. — SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the dataset without simply duplicating data. This is effective for fraud detection because it provides the model with more diverse examples of fraudulent transactions, reducing the bias toward the majority class.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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