NCP-GENL Fine-Tuning Practice Question
What is the primary risk of 'catastrophic forgetting' during the fine-tuning process?
⚠ Common exam trap
Candidates often confuse catastrophic forgetting with overfitting to the training set, missing that catastrophic forgetting specifically involves losing broad, generalized pre-trained capabilities.
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
✓
The model loses the general knowledge acquired during pre-training
Catastrophic forgetting occurs when a model is updated on new, narrow tasks, causing it to lose the broad, generalized knowledge acquired during its extensive pre-training phase. In production, this renders the model useless for its original intended tasks. Mitigating this risk is crucial for businesses that need to maintain multi-purpose model capabilities while still achieving performance gains in specialized domains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model becomes unable to produce responses in the desired language
Why it's wrong here
While language loss can occur, it is a symptom of broader knowledge degradation, not the primary definition. Catastrophic forgetting refers to the loss of the model's learned representation of facts and reasoning capabilities, not just linguistic fluency. The loss is more systemic and affects the model's general logical reasoning.
- ✓
The model loses the general knowledge acquired during pre-training
Why this is correct
Catastrophic forgetting refers to the phenomenon where a model's weights are modified so drastically that it loses its proficiency in tasks it was previously capable of. This happens when the fine-tuning loss prioritizes the new dataset too heavily, forcing the weights to shift away from the general knowledge base established during pre-training.
- ✗
The model's inference speed increases significantly
Why it's wrong here
Catastrophic forgetting is an accuracy and capability issue, not a performance or latency issue. It has no direct impact on inference speed. Inference latency is determined by model size, hardware architecture, and quantization, whereas the quality of the model's outputs is determined by the weights learned during fine-tuning.
- ✗
The model begins to output only empty strings
Why it's wrong here
Outputting empty strings is typically a sign of a logic error or a failure in the generation loop, not catastrophic forgetting. A model experiencing catastrophic forgetting will still produce text, but the quality of that text will be significantly lower in terms of factual accuracy, logical consistency, and task adherence.
About these practice questions
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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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