NCA-GENL Software Development Practice Question
Which THREE factors should a developer consider when choosing between FP16 and INT8 quantization for a production LLM deployment?
⚠ Common exam trap
Candidates often assume INT8 is always superior because it is faster, ignoring that hardware support for specific tensor operations and perplexity degradation are critical constraints that can disqualify INT8 for certain use cases.
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
✓
Hardware support for specific tensor core operations.
Choosing the correct precision balance is critical for optimizing LLM performance. FP16 offers a good compromise between quality and speed, while INT8 provides significant memory savings and increased throughput at the cost of potential precision loss. Understanding these trade-offs allows developers to align their model deployment with hardware constraints and accuracy requirements, ensuring the application maintains acceptable quality while meeting performance targets for end-user response times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hardware support for specific tensor core operations.
Why this is correct
NVIDIA GPUs have varying support for different precision types in their Tensor Cores. Choosing a precision format that the underlying hardware can accelerate natively is crucial for achieving maximum throughput, as using non-optimized formats results in significant performance degradation during the inference execution phase.
- ✓
The potential impact on model perplexity or accuracy.
Why this is correct
Quantization introduces noise that can degrade the model's output quality. A developer must perform calibration to ensure that the drop in perplexity or task-specific accuracy is within acceptable bounds for the business application, as extreme quantization can lead to nonsensical results in high-stakes scenarios.
- ✗
The availability of sufficient cooling for the server.
Why it's wrong here
While power consumption is related to GPU compute intensity, precision choice is not a direct factor in the physical cooling capacity requirements of a server rack. The choice between FP16 and INT8 is an algorithmic and architectural decision, not a primary driver of thermal management design.
- ✓
The memory overhead of the model weights.
Why this is correct
Lower precision formats significantly reduce the memory footprint of the model weights. This is critical for fitting large models into GPU VRAM or reducing the number of GPUs needed for deployment, directly impacting the cost-efficiency and scalability of the production inference environment.
- ✗
The compatibility with standard CSV file formats.
Why it's wrong here
Quantization precision has no relationship with the data storage format of the input or output files. CSV compatibility is a matter of software parsing and data engineering, not a constraint that impacts the choice of model precision during the development of the inference backend.
About these practice questions
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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 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.