NCP-AIO Troubleshooting and Optimization Practice Question
Exhibit
Error: CUDA_ERROR_OUT_OF_MEMORY GPU Memory Usage: 31.8GB / 32.0GB Batch Size: 128 Precision: FP32 Model Architecture: Transformer-based
Refer to the exhibit. The training job fails with a CUDA OOM error. Given the memory profile, which optimization strategy provides the most immediate relief while maintaining model performance?
⚠ Common exam trap
Candidates often choose complex model architecture changes like gradient checkpointing or model parallelism, which are harder to implement than enabling AMP for immediate memory relief.
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
✓
Enable Automatic Mixed Precision (AMP).
Switching to Mixed Precision (FP16/BF16) effectively halves the memory requirement for model activations and gradients. This is a standard optimization strategy for Transformer models when GPU memory capacity is the primary constraint. By reducing the memory footprint of floating-point operations, researchers can fit larger batch sizes or deeper architectures into the same GPU memory, significantly improving training efficiency and throughput without sacrificing convergence stability.
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 batch size to 256.
Why it's wrong here
Increasing the batch size will consume even more GPU memory, which will exacerbate the OOM error. Since the current usage is already at 31.8GB of 32GB available, any further increase in memory allocation will cause immediate failure during the next forward pass of the model.
- ✓
Enable Automatic Mixed Precision (AMP).
Why this is correct
Mixed precision reduces memory usage by using FP16 or BF16 for most operations, which requires less memory than FP32. This approach allows the training process to utilize significantly less VRAM, providing the necessary headroom to avoid the current OOM error while keeping the model training pipeline functional.
- ✗
Disable all CUDA kernels.
Why it's wrong here
Disabling CUDA kernels is not a viable strategy as it would prevent the GPU from performing any calculations. The model requires these kernels to execute the forward and backward passes. Without them, the code will default to CPU execution, which is orders of magnitude slower.
- ✗
Replace the GPU with a higher-clocked model.
Why it's wrong here
Increasing the GPU clock frequency improves compute speed but does not increase total available VRAM. An OOM error is a capacity issue, not a compute speed issue, so upgrading the clock speed will not resolve the lack of memory space required to store the model tensors.
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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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