NCA-GENL Software Development Practice Question
A developer is packaging a generative AI application that must run inference on-premises with NVIDIA GPUs and also expose an OpenAI-compatible HTTP API so existing client code works unchanged. Which two components should the developer use together to meet these requirements? (Choose two.)
⚠ Common exam trap
The trap here is assuming that an optimization library or a guardrails framework can substitute for the serving runtime and API layer that actually expose compatible endpoints.
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
✓
NVIDIA Triton Inference Server as the serving runtime hosting the NIM model artifacts.
The requirement pairs an on-premises GPU deployment with OpenAI-compatible APIs. NIM supplies optimized model microservices with those compatible endpoints, while Triton Inference Server provides the runtime that executes the model artifacts and manages batching and GPU resources. Together they deliver both the API contract and the execution environment without changes to existing client code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NVIDIA NeMo Guardrails as the component that translates OpenAI API calls into model inputs.
Why it's wrong here
NeMo Guardrails adds safety and topical controls around conversational flows; it is not an API translation layer and does not convert OpenAI-format requests into model inputs. Using it for protocol translation misuses the tool and would not by itself expose compatible endpoints. It can complement a deployment but does not satisfy either stated requirement on its own.
- ✗
NVIDIA TensorRT as the reverse proxy that routes client requests to model replicas.
Why it's wrong here
TensorRT is an inference optimizer and runtime for building optimized engines; it is not a reverse proxy and performs no request routing. Confusing it with a networking component leads to a design that cannot distribute traffic. It may be part of the optimization path under Triton, but it does not provide the compatible HTTP surface or proxy behavior.
- ✗
NVIDIA CUDA Toolkit as the HTTP server exposing the compatible endpoints.
Why it's wrong here
The CUDA Toolkit provides compilers and libraries for GPU programming, not an HTTP inference server. It has no role in exposing OpenAI-compatible routes or managing model serving. While CUDA underpins GPU execution, it cannot fulfill the API compatibility or serving requirements described in the scenario.
- ✓
NVIDIA Triton Inference Server as the serving runtime hosting the NIM model artifacts.
Why this is correct
Triton Inference Server is the runtime that hosts and executes the model artifacts behind the compatible API, handling batching, concurrency, and GPU scheduling. NIM microservices are delivered as Triton-hosted deployments, so pairing the compatible API layer with Triton as the execution engine matches both the on-premises GPU requirement and the API requirement.
- ✓
NVIDIA NIM for the model microservice providing the OpenAI-compatible inference endpoints.
Why this is correct
NIM packages optimized model microservices and exposes OpenAI-compatible APIs, so existing client code that targets those routes continues to work without modification. It runs on NVIDIA GPUs in on-premises environments, satisfying the deployment constraint. This makes it a correct component for delivering the compatible HTTP surface the scenario demands.
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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.