The correct answer involves three techniques: undersampling the majority class, oversampling the minority class (such as with SMOTE), and applying class weights in the loss function. These methods directly address class imbalance by either rebalancing the dataset or adjusting the model’s learning focus, ensuring the minority fraud cases are not ignored during training. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical SageMaker workflows for imbalanced data, often appearing in scenario-based questions where accuracy is a trap metric—precision-recall or AUC is preferred instead. A common mistake is to train only on the majority class or rely on accuracy, which the exam explicitly penalizes. Memory tip: think “U-O-W” for Undersample, Oversample, and Weights—three pillars to fight imbalance.
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 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.
Options A, C, and E are correct. Oversampling the minority class (e.g., SMOTE) and undersampling the majority class help balance the dataset. Using class weights in the loss function penalizes misclassifications of minority class more. Option B is incorrect because using accuracy as the metric can be misleading for imbalanced datasets; precision-recall or AUC is better. Option D is incorrect because training only on majority class would ignore minority class entirely.
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
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.
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. — Options A, C, and E are correct. Oversampling the minority class (e.g., SMOTE) and undersampling the majority class help balance the dataset. Using class weights in the loss function penalizes misclassifications of minority class more. Option B is incorrect because using accuracy as the metric can be misleading for imbalanced datasets; precision-recall or AUC is better. Option D is incorrect because training only on majority class would ignore minority class entirely.
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.
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Question Discussion
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