- A
One-vs-rest encoding
Why wrong: One-vs-rest is for multi-class.
- B
Use class weights in the loss function
Class weights penalize minority errors more.
- C
L1 regularization
Why wrong: L1 may not help with imbalance.
- D
Principal component analysis (PCA)
Why wrong: PCA is for dimensionality reduction.
Question 990 of 1,755
Combining SMOTE and Class Weights for Imbalanced Classification
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 uses Amazon SageMaker to train a model for fraud detection. The training data is highly imbalanced. The data scientist uses SMOTE to oversample the minority class. However, the model still has poor recall on the minority class. Which additional technique should the data scientist consider?
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
✓
Use class weights in the loss function
SMOTE generates synthetic samples for the minority class, but it does not directly address the model's tendency to prioritize the majority class during training. By assigning higher class weights to the minority class in the loss function, the model penalizes misclassifications of minority samples more heavily, which directly improves recall on that class. This technique is especially effective when combined with oversampling, as it forces the optimizer to focus on the underrepresented class during gradient updates.
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.
- ✗
One-vs-rest encoding
Why it's wrong here
One-vs-rest is for multi-class.
- ✓
Use class weights in the loss function
Why this is correct
Class weights penalize minority errors more.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
L1 regularization
Why it's wrong here
L1 may not help with imbalance.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume SMOTE alone is sufficient to fix imbalance, but the exam tests the understanding that oversampling must be paired with a cost-sensitive learning technique like class weighting to directly influence the model's optimization objective.
Detailed technical explanation
How to think about this question
Class weighting works by scaling the loss contribution of each sample inversely proportional to its class frequency; for example, in binary cross-entropy, the loss for a minority sample is multiplied by a weight >1, increasing the gradient magnitude for that sample. In practice, the weight ratio is often set to the inverse of class frequencies (e.g., 0.9/0.1 = 9 for a 10% minority), but tuning via cross-validation is critical to avoid overfitting to synthetic samples. A real-world scenario is credit card fraud detection, where even a 1% recall improvement can save millions, and class weights are used alongside SMOTE to balance precision and recall.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 class weights in the loss function — SMOTE generates synthetic samples for the minority class, but it does not directly address the model's tendency to prioritize the majority class during training. By assigning higher class weights to the minority class in the loss function, the model penalizes misclassifications of minority samples more heavily, which directly improves recall on that class. This technique is especially effective when combined with oversampling, as it forces the optimizer to focus on the underrepresented class during gradient updates.
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
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