NCA-GENL Software Development Practice Question
Exhibit
config.pbtxt:
backend: "tensorrt"
parameters {
key: "precision_mode"
value: { string_value: "fp8" }
}Refer to the exhibit. What is the technical implication of using the specified 'fp8' precision mode in this model configuration?
⚠ Common exam trap
Candidates often assume FP8 is just a version of INT8. They fail to realize FP8 offers a superior dynamic range, which is why it is preferred for LLMs over the more restrictive integer formats.
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
✓
It provides a better balance of accuracy and speed than INT8.
FP8 precision takes advantage of the hardware-native support for 8-bit floating-point math in the latest NVIDIA GPU architectures (like Hopper). This mode provides a higher dynamic range than INT8 quantization, making it easier to maintain model accuracy while achieving superior throughput and memory efficiency. It is the current state-of-the-art for high-performance LLM deployment, balancing the need for speed with the requirement for high-fidelity generative output in large-scale enterprise services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It will disable Tensor Core utilization on the GPU.
Why it's wrong here
FP8 is specifically designed to work with the Tensor Cores on modern NVIDIA architectures. Enabling this mode actually increases the usage of these cores, as they are optimized for 8-bit floating point matrix operations, providing a massive performance boost compared to standard FP16 or FP32 precision modes.
- ✗
It requires the model to be retrained from scratch.
Why it's wrong here
FP8 is a post-training optimization. The model does not need to be retrained from scratch; it only needs to be converted and calibrated to the FP8 format. This allows developers to take existing, well-performing models and immediately benefit from hardware acceleration without the massive compute cost of training.
- ✓
It provides a better balance of accuracy and speed than INT8.
Why this is correct
FP8 offers a wider dynamic range than INT8, which is fixed-point. This makes FP8 much more resilient to accuracy loss during quantization. It delivers the speed and memory efficiency benefits of low-bit arithmetic while maintaining performance levels closer to full FP16 or FP32 implementations.
- ✗
It is only compatible with CPU-based inference engines.
Why it's wrong here
FP8 is a GPU-centric optimization technology. It is not supported or intended for CPU inference, which typically relies on AVX-512 or other integer-based instructions. The entire premise of using FP8 with TensorRT is to leverage the specialized hardware acceleration units available exclusively on modern NVIDIA GPUs.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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 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.