NCA-GENL Experimentation Practice Question
Exhibit
Config: { 'model': 'nemotron-3-8b', 'precision': 'fp16', 'batch_size': 32, 'max_seq_len': 4096, 'gpu_mem_usage': '98%' }Refer to the exhibit. The experiment fails with an Out-of-Memory (OOM) error during the second epoch. Given the configuration, which change is most effective for immediate stabilization?
⚠ Common exam trap
Candidates often suggest increasing memory or changing hardware types. They fail to realize that batch size is the most direct control variable for memory consumption in a standard training loop.
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
✓
Reduce batch_size to 16.
The OOM error indicates the memory footprint exceeds the available VRAM, likely due to activation growth or KV cache accumulation. Reducing the batch size is the most direct way to lower memory consumption per iteration. Experimentation requires iterative adjustment of hyperparameters; scaling back memory-intensive settings allows the job to complete successfully, providing a baseline from which performance optimizations like gradient accumulation can then be systematically applied to restore throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch precision to FP32.
Why it's wrong here
Switching to FP32 doubles the memory requirement for model weights and activation buffers. Since the system is already at 98% memory usage, this change would immediately trigger an OOM error at the start of the next epoch, preventing any further progress in the training experiment cycle.
- ✗
Increase batch_size to 64.
Why it's wrong here
Increasing the batch size linearly increases the memory required for activations. Since the current experiment is failing at 98% capacity with a batch size of 32, doubling the size will certainly exceed available VRAM, leading to an immediate crash before the model can process any additional data.
- ✓
Reduce batch_size to 16.
Why this is correct
Reducing the batch size decreases the memory demand for forward and backward passes. This allows the process to fit within the GPU constraints, enabling the experiment to continue and finish. This trade-off is standard in LLM experimentation when balancing hardware limitations against model size and sequence length requirements.
- ✗
Increase max_seq_len to 8192.
Why it's wrong here
Sequence length has a quadratic impact on memory consumption for attention mechanisms. Increasing it while already at 98% utilization is counter-productive. This would exponentially increase the memory pressure, causing the training process to fail even earlier than it currently does during the initial phases of the computation.
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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 NCA-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 NCA-GENL exam.