MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to build a binary classification model. The dataset is highly imbalanced, with 95% negative class and 5% positive class. Which technique should be used to address the class imbalance?
⚠ Common exam trap
Watch out — candidates often choose under-sampling (Option C) as a quick fix, but the exam tests understanding that under-sampling discards data and can hurt performance, while weighted loss preserves all data and is the preferred technique in SageMaker for imbalanced classification.
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 a weighted loss function during training.
Using a weighted loss function (e.g., class weights in SageMaker's built-in XGBoost or custom PyTorch loss) assigns a higher penalty to misclassifications of the minority positive class. This directly addresses the 95:5 imbalance by making the model more sensitive to the positive class during gradient updates, without discarding data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a weighted loss function during training.
Why this is correct
Weighted loss penalizes errors on minority class more heavily.
- ✗
Use accuracy as the primary evaluation metric.
Why it's wrong here
Accuracy can be misleading in imbalanced datasets.
- ✗
Perform random under-sampling of the majority class.
Why it's wrong here
Under-sampling may discard useful information.
- ✗
Remove all examples from the majority class.
Why it's wrong here
This severely reduces data and can lead to underfitting.
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