NCA-GENL Software Development Practice Question
A developer is evaluating a fine-tuned LLM with NVIDIA NeMo and observes that evaluation loss keeps decreasing while downstream task accuracy plateaus and then declines. Which action should the developer take to address this?
⚠ Common exam trap
The trap here is treating falling loss as proof of improvement, when declining task accuracy reveals overfitting that more training or a higher learning rate would worsen.
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 early stopping based on the downstream validation metric and consider regularization such as dropout or weight decay.
A widening gap between decreasing evaluation loss and declining downstream accuracy is the classic signature of overfitting. Early stopping on the task metric preserves the best generalizing checkpoint, and regularization techniques such as dropout or weight decay constrain the model so optimization progress translates into real task gains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Extend training for more epochs so the loss can converge further.
Why it's wrong here
More epochs will continue reducing training loss while task accuracy degrades further, deepening overfitting. The plateau followed by decline already shows the model is memorizing rather than generalizing, so additional training time is exactly the wrong direction for this scenario.
- ✗
Increase the learning rate to escape the plateau.
Why it's wrong here
Raising the learning rate when validation accuracy is already declining typically worsens overfitting and destabilizes training. The divergence between falling loss and falling task accuracy signals overfitting, not an optimization stall, so a larger step size would amplify the problem rather than resolve it.
- ✓
Apply early stopping based on the downstream validation metric and consider regularization such as dropout or weight decay.
Why this is correct
Falling loss with declining task accuracy indicates overfitting to the training distribution. Early stopping on the validation metric halts training at the best generalizing point, while dropout or weight decay constrain the model. Together they restore alignment between optimization loss and real task performance.
- ✗
Switch the evaluation metric to training loss for consistency.
Why it's wrong here
Using training loss as the selection metric hides the generalization gap that the downstream metric exposes. The goal is to maximize real task accuracy, so replacing the metric with the one that is already misleading removes the signal needed to detect overfitting and select a good checkpoint.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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