NCA-GENL Software Development Practice Question
A developer is building a Retrieval-Augmented Generation (RAG) pipeline using NVIDIA NIM microservices. They need to ensure that the retriever returns the most relevant documents for a given query. Which two components should they optimize? (Choose two.)
⚠ Common exam trap
The trap here is conflating generation parameters with retrieval components, leading to choices that do not affect which documents are retrieved.
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
✓
The embedding model used to vectorize documents and queries
Retrieval quality in RAG depends heavily on the embedding model that encodes semantic meaning and the vector database that efficiently finds nearest neighbors. Optimizing both ensures that the most relevant documents are surfaced. Other parameters like temperature or attention heads affect generation, not retrieval, and tokenizer vocabulary is fixed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The LLM's temperature setting
Why it's wrong here
Temperature controls randomness in generation, not retrieval. It affects how the LLM uses retrieved documents but does not influence which documents are retrieved. Optimizing temperature may change output style but not the relevance of the retrieved set, so it is not a correct choice here.
- ✓
The embedding model used to vectorize documents and queries
Why this is correct
The embedding model determines the quality of semantic representations. A model fine-tuned for the domain or a state-of-the-art model like NVIDIA's NV-Embed can significantly improve retrieval relevance. Choosing the right embedding model is foundational to RAG performance, making this a correct choice.
- ✓
The vector database index and similarity search algorithm
Why this is correct
The vector database's index type (e.g., HNSW, IVF) and similarity metric (e.g., cosine, dot product) directly affect retrieval accuracy and speed. Proper tuning of these parameters ensures that the most relevant vectors are returned. This is a critical component to optimize for relevant document retrieval.
- ✗
The tokenizer's vocabulary size
Why it's wrong here
Tokenizer vocabulary size is determined by the model and affects tokenization efficiency, not retrieval relevance. While it can impact embedding quality indirectly, it is not a component typically optimized for retrieval. The embedding model and vector index are the primary levers.
- ✗
The number of attention heads in the LLM
Why it's wrong here
Attention heads are part of the LLM architecture and are fixed during inference. They affect how the model processes context but do not change the retrieval step. This is not a tunable parameter for improving document relevance in a RAG pipeline.
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.