NCA-GENL Software Development Practice Question
A developer is building a retrieval-augmented generation service and needs to embed millions of document chunks and run low-latency similarity search over them on GPU. They want a library that handles both index construction and search with GPU acceleration. Which NVIDIA component should they use?
⚠ Common exam trap
The trap here is conflating embedding generation with vector search, assuming that a service which produces embeddings also performs similarity retrieval over them.
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 cuVS, a GPU-accelerated library for vector search and clustering that builds and queries similarity indexes.
The task splits into encoding and retrieval, and the retrieval half demands an index plus fast nearest-neighbor search. cuVS supplies GPU-accelerated vector search and clustering, handling index build and query for large embedding sets. Embedding microservices, TensorRT-LLM, and Triton each address adjacent concerns such as encoding, generation optimization, or orchestration, but none provides the vector index and search capability the scenario requires.
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 cuVS, a GPU-accelerated library for vector search and clustering that builds and queries similarity indexes.
Why this is correct
cuVS provides GPU-accelerated vector search and clustering primitives, including index build and nearest-neighbor query paths. It is designed exactly for large-scale embedding search where low latency matters, covering both index construction and search. That matches the developer's dual requirement of embedding millions of chunks and serving fast similarity lookups on GPU.
- ✗
NVIDIA NeMo Retriever embedding microservices for generating document and query embeddings at scale.
Why it's wrong here
NeMo Retriever microservices produce high-quality embeddings, but they are the encoding stage rather than the similarity search index. The developer still needs a component that stores vectors and answers nearest-neighbor queries with GPU acceleration. Using only embedding services leaves the index construction and low-latency search requirement unmet, so it addresses half the pipeline.
- ✗
NVIDIA Triton Inference Server with an ensemble pipeline that chains preprocessing and postprocessing steps.
Why it's wrong here
Triton orchestrates model execution and ensembles, but it is not a vector index. It can host an embedding model and a reranker behind endpoints, yet similarity search over millions of vectors requires an index structure with GPU-accelerated distance computation. Triton would need an external retrieval library underneath, so it does not itself satisfy the search requirement.
- ✗
NVIDIA TensorRT-LLM for compiling the embedding model into an optimized engine.
Why it's wrong here
TensorRT-LLM optimizes generative transformer inference and can accelerate embedding model execution, but it does not build or query a vector index. The scenario's core need is similarity search over millions of stored vectors, which is an index and retrieval problem. Compiling the encoder alone would speed embedding generation while leaving the search stage without a GPU-accelerated index.
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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
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