NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A data scientist is working with a dataset that has a highly skewed distribution, with one class representing only 2% of the samples. They are training a binary classifier and notice that the model predicts the majority class almost exclusively. Which technique is most appropriate to address this issue?
⚠ Common exam trap
The trap here is thinking that changing the model architecture or learning rate will fix class imbalance, but the core issue is the loss function's bias toward the majority class.
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 class weighting in the loss function
Class imbalance causes models to favor the majority class because the loss function is dominated by those samples. Applying class weights adjusts the loss to penalize minority class errors more heavily, which balances the influence of each class during training. This is a direct and effective technique for improving minority class detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the minority class samples
Why it's wrong here
Removing minority class samples would eliminate the very class the model needs to learn to predict, making the problem worse. The goal is to improve detection of the minority class, not to ignore it. This approach would result in a model that never predicts the minority class.
- ✓
Apply class weighting in the loss function
Why this is correct
Class weighting assigns a higher penalty to misclassifying the minority class, which encourages the model to pay more attention to it. This directly addresses the imbalance by adjusting the loss contribution of each class. It is a standard and effective method to improve minority class recall without altering the data distribution.
- ✗
Use a simpler model architecture
Why it's wrong here
Simplifying the model might reduce overfitting, but it does not address class imbalance. The model would still be biased toward the majority class because the training signal is dominated by those examples. A simpler model may even underfit and fail to capture minority class patterns.
- ✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate affects optimization speed and stability, but it does not address the underlying class imbalance. The model would still be biased toward the majority class because the loss is dominated by those samples. A higher learning rate might even cause divergence or worsen performance.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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