MLS-C01 Modeling Practice Question
A machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)
⚠ Common exam trap
The trap here is that candidates may incorrectly select focal loss (Option D) as a standalone answer, but the question requires exactly two correct options, and class weighting (Option E) is a more straightforward loss-modification technique that is explicitly tested in the MLS-C01 exam as a standard approach 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
✓
Oversample the minority classes in the training data.
Oversampling the minority classes (Option A) directly addresses class imbalance by replicating samples from underrepresented classes, giving the model more exposure to them during training. This is a standard data-level technique that helps the ResNet-50 model learn discriminative features for minority classes without altering the loss function or model architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Oversample the minority classes in the training data.
Why this is correct
Oversampling increases representation of minority classes, balancing the training set.
- ✗
Reduce the batch size to increase the frequency of weight updates.
Why it's wrong here
Batch size affects training dynamics but does not directly address class imbalance.
- ✗
Increase the number of layers in the model.
Why it's wrong here
Adding layers increases model capacity but does not directly address imbalance and may lead to overfitting.
- ✗
Switch to a focal loss function.
Why it's wrong here
Focal loss is designed for single-stage object detectors and is less commonly used in multi-class classification with pre-trained models; class weighting is more direct.
- ✓
Use class weighting in the loss function.
Why this is correct
Class weighting penalizes misclassifications of minority classes more heavily, which helps the model learn from them.
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