NCA-GENL Software Development Practice Question
A developer needs to serve a quantized Llama model on an NVIDIA GPU and wants the runtime to automatically select the fastest available execution kernels for the detected GPU architecture. Which approach aligns with the NVIDIA inference stack for this requirement?
⚠ Common exam trap
The trap here is believing that any GPU-enabled framework automatically performs architecture-aware kernel selection, when that behavior comes from an ahead-of-time compiled inference engine.
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
✓
Compile the model with TensorRT-LLM to produce an engine that contains architecture-tuned kernels and let the runtime pick the best ones for the GPU present.
TensorRT-LLM compiles model graphs into engines containing kernels tuned for specific GPU architectures, and at runtime it selects from the available tactics for the hardware present. Building the engine for the deployment GPU ensures the fastest kernels are candidates. This gives the developer optimized execution without hand-authoring CUDA code or losing performance to eager framework dispatch.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write custom CUDA kernels for each target GPU and dispatch them manually at runtime.
Why it's wrong here
Hand-writing and manually dispatching CUDA kernels for every target architecture is a large maintenance burden and duplicates work that optimized inference runtimes already perform. It also risks missing architecture-specific optimizations. The requirement is automatic kernel selection, which purpose-built inference engines provide without custom kernel authoring.
- ✗
Convert the model to ONNX and rely on the default CPU execution provider.
Why it's wrong here
The default CPU execution provider ignores the NVIDIA GPU entirely, so no GPU kernels are selected at all. Even with a GPU execution provider, the scenario calls for the NVIDIA inference stack that performs architecture-aware tactic selection. Falling back to CPU defeats the purpose of deploying on an NVIDIA GPU and would be dramatically slower.
- ✗
Run the model in eager execution mode on PyTorch without any compilation step.
Why it's wrong here
Eager execution runs operations one at a time through the framework dispatcher and does not perform the ahead-of-time kernel fusion and tactic selection that compiled inference engines do. It will function, but it will not automatically choose the fastest available kernels for the GPU architecture. This approach sacrifices the performance the scenario requires.
- ✓
Compile the model with TensorRT-LLM to produce an engine that contains architecture-tuned kernels and let the runtime pick the best ones for the GPU present.
Why this is correct
TensorRT-LLM builds engines containing kernels tuned for the target GPU architecture, and the runtime selects among available tactics at execution time. Building for the deployment architecture ensures the best kernels are present, so the developer gets optimized execution without writing kernels. This directly matches the requirement for automatic selection of the fastest kernels on the detected GPU.
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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.