NCP-AIO Troubleshooting and Optimization Practice Question
Exhibit
Error: CUDA error: out of memory. Total allocated memory: 31.8 GB. Reserved memory: 32.0 GB. Current batch size: 128.
Refer to the exhibit. The training job fails with an OOM error. Which optimization strategy will most effectively resolve this while maintaining model convergence?
⚠ Common exam trap
Candidates frequently try to resolve OOM errors by simply reducing the batch size without realizing it can negatively impact model convergence and training accuracy.
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
✓
Implement Gradient Accumulation to simulate a larger batch size.
Memory management is central to deep learning stability. When a model exceeds physical VRAM, gradient accumulation allows for larger effective batch sizes without increasing memory footprint. By accumulating gradients over multiple small steps and performing a single weight update, the model effectively sees a larger batch size, maintaining the convergence characteristics of the original design while fitting within the strict physical memory constraints of the GPU hardware.
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 learning rate to compensate for smaller batches.
Why it's wrong here
Increasing the learning rate is not a substitute for proper memory management. While it changes the optimization dynamics, it does not address the fundamental OOM error caused by the memory footprint of the current batch size, and it likely leads to unstable model convergence or divergence.
- ✓
Implement Gradient Accumulation to simulate a larger batch size.
Why this is correct
Gradient accumulation allows you to simulate a large batch size by breaking it into smaller chunks that fit in GPU memory. You perform multiple forward/backward passes and accumulate the gradients, updating weights only after reaching the target batch size, effectively bypassing the physical memory limitation.
- ✗
Disable mixed-precision training to reduce memory overhead.
Why it's wrong here
Mixed-precision training (FP16/BF16) actually reduces memory usage compared to full FP32 training. Disabling it would increase the memory footprint, as FP32 tensors consume twice the space of FP16 tensors, making the OOM condition significantly worse rather than providing a solution to the existing error.
- ✗
Clear the GPU cache using torch.cuda.empty_cache() after every iteration.
Why it's wrong here
Emptying the cache releases fragmented memory back to the OS, but it does not prevent OOM errors caused by the active tensors in a batch. Since the OOM occurs during the forward/backward pass, clearing the cache after the crash is ineffective for training stability or resource management.
Visual reference
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 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-AIO 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-AIO exam.