NCA-GENL Experimentation Practice Question
An AI researcher is testing a new LLM architecture on an NVIDIA DGX system. They observe that increasing the batch size leads to memory OOM errors despite available GPU utilization headroom. Which experimentation strategy should be employed first to isolate the bottleneck?
⚠ Common exam trap
Candidates often assume Out-Of-Memory errors during batch size increases are caused by model weights, ignoring that activation memory during the forward pass scales with batch size and sequence length.
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 checkpointing to trade compute for memory.
Memory allocation during deep learning training is often impacted by activation storage rather than just model weights. By systematically reducing the batch size or implementing gradient checkpointing, the researcher can determine if the OOM is due to peak activation memory usage. This experimentation phase is critical for optimizing hardware utilization and ensuring stable training cycles in high-throughput enterprise environments.
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 accelerate convergence speed.
Why it's wrong here
Adjusting the learning rate primarily affects model convergence and optimization stability rather than GPU memory consumption. Increasing the rate will not resolve the Out-of-Memory (OOM) error; instead, it might lead to training divergence if the learning rate becomes too high for the current batch size settings.
- ✗
Switch to a different optimizer like SGD instead of Adam.
Why it's wrong here
While switching optimizers changes memory requirements due to state storage, it does not directly address the peak activation bottleneck causing the OOM. The primary constraint in large-scale LLM training is often the memory required to store activations for backpropagation, which remains high regardless of the specific optimizer selected.
- ✓
Implement gradient checkpointing to trade compute for memory.
Why this is correct
Gradient checkpointing is a standard experimentation technique to reduce memory usage by discarding intermediate activations during the forward pass and recomputing them during backpropagation. This effectively trades a small amount of additional compute time for a significant reduction in peak GPU memory usage, resolving the OOM error.
- ✗
Upgrade the NVIDIA driver version on the DGX host.
Why it's wrong here
Driver upgrades are essential for security and feature support but do not fundamentally change the memory footprint of a running training job. If the model architecture requires more memory than the GPU provides, a driver update will not rectify the underlying resource constraint during an experiment.
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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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