NCP-AIO Troubleshooting and Optimization Practice Question
Which TWO of the following actions should be taken to optimize GPU memory usage when encountering Out-of-Memory (OOM) errors during model training?
⚠ Common exam trap
Candidates often select 'increasing batch size' or 'adding more hardware' as solutions. These actually exacerbate OOM errors or ignore the requirement to optimize memory within existing hardware constraints.
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.
OOM errors are common in deep learning when the model size, activation memory, and batch size exceed VRAM capacity. Implementing gradient checkpointing and mixed-precision training are standard industry practices to manage memory pressure. Mastery of these techniques is essential for AI operations engineers to maintain system stability and enable the training of large-scale models without requiring immediate hardware upgrades.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement gradient checkpointing to trade compute for memory.
Why this is correct
Gradient checkpointing stores only a subset of activations and recomputes the rest during the backward pass. This significantly reduces the memory footprint of the activation graph, allowing for larger models or batch sizes to fit in memory at the cost of additional compute cycles during training.
- ✗
Increase the number of CPU threads in the data loader.
Why it's wrong here
Increasing CPU threads in the data loader improves the speed at which data is staged, but it does not reduce the VRAM footprint of the model itself. In fact, aggressive prefetching might temporarily increase memory usage, potentially worsening an existing OOM condition during training iterations.
- ✓
Use mixed-precision training (FP16/BF16) to reduce weight storage.
Why this is correct
Mixed-precision training reduces the memory footprint of model weights and intermediate activations by using 16-bit floating-point types instead of 32-bit. This effectively doubles the available memory for activations and batch sizes, serving as a primary strategy for fitting complex architectures into limited GPU VRAM capacities.
- ✗
Disable the use of NCCL to reduce inter-node memory overhead.
Why it's wrong here
Disabling NCCL would break distributed training entirely and does not meaningfully impact the VRAM consumption of the local model. NCCL's memory overhead is generally managed by the library and is necessary for synchronization; removing it is not a valid strategy for resolving local OOM issues.
- ✗
Switch from the Adam optimizer to Stochastic Gradient Descent (SGD).
Why it's wrong here
While SGD uses less memory than Adam because it lacks state buffers for momentum, switching optimizers can drastically change training convergence and model accuracy. This is a model tuning decision, not a primary infrastructure optimization strategy for resolving memory pressure compared to gradient checkpointing or precision settings.
About these practice questions
One of 309 original NCP-AIO practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.