NCA-GENL Core Machine Learning and AI Knowledge Practice Question
Which of the following describes the 'Warm-up' phase in the context of training deep neural networks?
⚠ Common exam trap
Candidates often mistake warm-up for a data augmentation technique or a method to increase model capacity, failing to recognize it as a stability technique for the initial training phase.
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
✓
Gradually increasing the learning rate at the start of training.
Warm-up is a technique where the learning rate starts at a very low value and is gradually increased over the first few hundred or thousand steps. This prevents the model from diverging early in the training process when weights are randomly initialized and gradients can be unstable. Proper warm-up is essential for successfully scaling the training of modern LLMs on large compute clusters, ensuring stable convergence from the very first iterations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-loading the model into the GPU cache.
Why it's wrong here
Pre-loading data is a latency optimization strategy, but it is not called 'warm-up'. Warm-up refers specifically to the dynamic adjustment of the learning rate hyperparameter during the early stages of the training loop to stabilize the gradients and ensure smooth convergence of the model's weight updates.
- ✓
Gradually increasing the learning rate at the start of training.
Why this is correct
The warm-up phase protects the model from unstable updates by keeping the learning rate low initially. This allows the model to stabilize its internal representations before applying the full, higher learning rate, which is critical for preventing divergence and achieving robust convergence during the initial stages of large-scale training.
- ✗
A method to reduce training time by caching gradients.
Why it's wrong here
Gradient accumulation is the technique for reducing the frequency of updates by caching gradients, not warm-up. Warm-up is not a speed optimization but a stability and convergence strategy that ensures the training process does not crash due to massive, erratic weight updates when starting from random initial states.
- ✗
Reducing the batch size to fit in memory.
Why it's wrong here
Adjusting batch size is a memory management strategy, not related to the warm-up phase. Changing batch sizes dynamically can disrupt the statistics of normalization layers; the warm-up process focuses purely on the learning rate schedule, which is the primary driver of training stability in early-phase model development.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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