NCP-GENL Fine-Tuning Practice Question
Exhibit
Error: CUDA out of memory. Tried to allocate 512.00 MiB. GPU 0 has 23.90 GiB total capacity. Memory used at last allocation: 23.50 GiB.
Refer to the exhibit. Which adjustment is the most immediate and effective way to resolve this OOM error while maintaining the same training architecture?
⚠ Common exam trap
Candidates often try to change model architecture or hardware settings first, ignoring that reducing the batch size is the most immediate and effective way to resolve OOM errors.
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
✓
Decrease the batch size per GPU.
The error indicates that the current batch size is too large for the available VRAM. Reducing the batch size is the most direct solution to free up enough memory for the model to continue training. If a larger effective batch size is required for convergence, the user can subsequently enable gradient accumulation to compensate for the reduced per-step batch size without increasing the memory footprint.
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 sequence length of the inputs.
Why it's wrong here
Increasing the sequence length significantly increases memory consumption, as attention mechanisms scale quadratically with length. Doing so would exacerbate the OOM error rather than resolve it, as the memory request would become even larger than the current allocation failure that caused the training to stop.
- ✓
Decrease the batch size per GPU.
Why this is correct
Decreasing the batch size is the most effective way to reduce memory consumption immediately. It lowers the VRAM overhead required for activation storage, allowing the model to fit within the existing hardware constraints. This is the standard approach to resolving OOM errors during the fine-tuning training process.
- ✗
Switch from mixed precision to full FP32 precision.
Why it's wrong here
Full FP32 precision increases memory usage by at least 2x compared to mixed precision (BF16 or FP16). Switching to FP32 would consume even more VRAM and would lead to a more severe OOM error, making it the opposite of the correct action needed to alleviate memory pressure.
- ✗
Disable the optimizer state checkpointing.
Why it's wrong here
Checkpointing is essential for memory-efficient training. Disabling it would cause the model to store all intermediate activations, consuming significantly more VRAM. This would increase the likelihood of OOM errors rather than preventing them, as checkpointing is one of the primary methods used to manage peak memory usage.
Visual reference
About these practice questions
Courseiva writes every NCP-GENL question from scratch — 352 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.