Question 1,493 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing model complexity (more trees) or adjusting thresholds post-training can fix class imbalance, when in fact the root cause is the skewed training data distribution, which requires a data-level technique like SMOTE.
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 oversample the rare class in the training data
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving overall accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of trees to 500
Why it's wrong here
More trees may not help with class imbalance.
- ✓
Apply SMOTE to oversample the rare class in the training data
Why this is correct
SMOTE creates synthetic samples for the minority class.
- ✗
Use stratified sampling only for the test set
Why it's wrong here
Stratified sampling in test set ensures representation but does not improve training.
- ✗
Reduce the decision threshold for the rare class to 0.1
Why it's wrong here
Changing threshold may increase recall but decrease precision and overall accuracy.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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.)
hard- ✓ A.Oversample the minority classes in the training data.
- B.Reduce the batch size to increase the frequency of weight updates.
- C.Increase the number of layers in the model.
- D.Switch to a focal loss function.
- ✓ E.Use class weighting in the loss function.
Why A: 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.
Last reviewed: Jun 11, 2026
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