NCA-GENL Experimentation Practice Question
A data scientist is preparing to perform hyperparameter tuning for a Retrieval-Augmented Generation (RAG) system. Which TWO parameters should be prioritized for experimentation to improve retrieval accuracy?
⚠ Common exam trap
Candidates often choose parameters related to model architecture or training rather than retrieval. They forget that RAG performance is primarily driven by how data is fetched and segmented.
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
✓
Chunk size
Retrieval accuracy in RAG systems is heavily influenced by the chunking strategy and the similarity search configuration. Experimenting with these two components allows developers to optimize the context provided to the LLM. Properly tuned retrieval ensures that the model has the most relevant information, which is the most critical factor in reducing hallucinations and improving the factual grounding of generated responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Chunk size
Why this is correct
Chunk size directly determines how much context is captured in each vector index entry. Too small, and the model lacks enough information; too large, and the content becomes noisy, leading to irrelevant retrieval. Finding the optimal balance through experimentation is essential for ensuring high-quality context retrieval for the LLM.
- ✗
Model quantization bit-width
Why it's wrong here
Quantization affects inference speed and memory usage, not the retrieval accuracy of the RAG system. While it is important for deployment, it does not change the semantic search results or the relevance of the retrieved documents, making it a secondary concern when tuning for core retrieval accuracy performance.
- ✓
Similarity search top-k
Why this is correct
The top-k parameter determines how many documents are passed to the generator. If top-k is too low, critical information might be missed; if too high, the context window might be flooded with irrelevant data, confusing the LLM. Testing different values is vital for balancing retrieval precision and recall.
- ✗
System prompt length
Why it's wrong here
System prompt length is a design choice related to instruction following, not document retrieval. While it affects how the model interprets retrieved context, it does not influence the search process itself. It should be tuned separately from the retrieval pipeline to optimize the model's overall reasoning and output quality.
- ✗
GPU clock speed
Why it's wrong here
GPU clock speed is a hardware configuration parameter that impacts the speed of computation. It does not affect the logical accuracy of the RAG retrieval process. Focusing on hardware settings during the retrieval experimentation phase misdirects efforts away from the algorithmic improvements that actually impact the quality of results.
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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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