NCA-GENL Software Development Practice Question
A developer is optimizing a retrieval-augmented generation (RAG) pipeline using NVIDIA TensorRT-LLM. They notice excessive latency during the document retrieval phase before the generation starts. Which optimization strategy is most effective for this bottleneck?
⚠ Common exam trap
Candidates often assume the bottleneck is in the LLM generation itself, failing to realize that slow retrieval (embedding search) is a common, distinct performance killer in RAG pipelines.
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
✓
Implement a GPU-accelerated vector database for similarity search.
Latency in RAG pipelines often stems from inefficient embedding lookups or serial processing. Moving vector search to a GPU-accelerated database or utilizing a cross-encoder for re-ranking ensures the model receives highly relevant chunks. This approach balances retrieval precision with speed, ensuring the LLM receives context without stalling the inference server, which is critical for real-time generative applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of transformer layers in the LLM.
Why it's wrong here
Adding transformer layers increases the computational overhead and memory footprint of the model, which exacerbates latency issues. This action does nothing to address the retrieval bottleneck occurring before the generative phase begins, potentially slowing down the system further during the inference phase.
- ✓
Implement a GPU-accelerated vector database for similarity search.
Why this is correct
Moving from CPU-based vector indexing to GPU-accelerated solutions like Faiss on NVIDIA hardware drastically reduces search latency. This optimization allows for parallel processing of vector embeddings, which is crucial when handling large datasets in RAG pipelines, effectively offloading the retrieval bottleneck from the host CPU.
- ✗
Use float64 precision for all vector embedding calculations.
Why it's wrong here
High-precision calculations such as float64 are unnecessary for most semantic search tasks and consume significantly more memory and compute cycles. Switching to lower precision formats like FP16 or INT8 is the standard practice to improve throughput and reduce latency in deep learning workflows.
- ✗
Decrease the context window size of the retrieval model.
Why it's wrong here
Reducing the context window limits the amount of information the LLM can process, potentially leading to lower quality answers and hallucinated outputs. While it might shave off milliseconds, it sacrifices the primary utility of the RAG pipeline, which relies on large, contextually rich documents.
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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
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.