NCP-AIO Troubleshooting and Optimization Practice Question
An AI researcher is running a training job using mixed precision (FP16/BF16). The loss function is diverging unexpectedly. What is the most likely culprit?
⚠ Common exam trap
Candidates often assume divergence is caused by a poor learning rate or bad initialization, overlooking the precision limitations inherent in standard FP16 training.
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
✓
The lack of loss scaling for FP16.
Mixed precision training uses lower precision formats to speed up math and reduce memory usage, but this can lead to numerical instability. Divergence in loss is a classic symptom of 'underflow' or 'overflow' issues occurring in FP16, where small gradients or large weight updates exceed the representable range. Implementing loss scaling is the standard industry technique to preserve the precision of gradients and ensure the training process remains stable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The GPU driver is too old for FP16.
Why it's wrong here
FP16 support has been standard for many generations of NVIDIA GPUs. A driver that is too old would generally prevent the job from even starting. If the job is running but diverging, it is a numerical issue inherent to the math, not a driver-level compatibility problem with the hardware.
- ✗
The learning rate is too high for mixed precision.
Why it's wrong here
While learning rate is important, loss divergence in mixed precision is primarily due to the limitations of the FP16 format itself. A high learning rate causes divergence in any precision mode. The specific challenge of mixed precision is the range of the floating-point values, which requires specialized handling like scaling.
- ✓
The lack of loss scaling for FP16.
Why this is correct
FP16 has a limited dynamic range. During backpropagation, many small gradient values can be rounded to zero (underflow). Loss scaling multiplies the loss before backpropagation, pushing the gradients into a representable range in FP16, then unscaling them before applying weight updates. This is critical for preventing divergence in mixed precision.
- ✗
The batch size is too large.
Why it's wrong here
Batch size affects convergence speed and stability, but not the specific divergence characteristics associated with floating-point precision formats. Divergence in mixed precision training is almost always a numerical precision issue. Modifying the batch size does not resolve the underlying problem of gradient underflow or overflow in the FP16 operations.
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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 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.