Free NVIDIA NCA-GENL Actual Exam Questions - Question 4 Discussion

D imo, since A and D focus on retraining or fine-tuning, which isn’t the core idea behind RAG. B fits better because it highlights the retrieval plus generation combo that makes RAG unique.

Option B, since it specifically includes the retrieval step alongside generation, unlike the others.

Maybe B, since it specifically mentions combining retrieval with generation, unlike A or D.

I think C can be ruled out since it’s just describing Transformer text generation generally, not the unique retrieval step RAG introduces. So it’s really between B and the training-focused options.

I’m with the crowd on B — it’s about adding a retrieval step to help the generator produce better responses by grabbing relevant info. A and D talk about retraining or fine-tuning, which is not what RAG directly does. C is too vague and doesn’t mention retrieval at all, so B clearly stands out as the only option that captures the retrieval plus generation combo. Plus, RAG’s strength is pulling in context dynamically, not changing model weights or just generating text blindly.

Guessing B here too, since RAG really stands for mixing retrieval with generation. Options A and D focus on training changes, which doesn’t line up with how RAG actually works in practice.

Maybe D, but I’m not convinced since fine-tuning usually means adjusting weights, while RAG is more about pulling relevant data during generation. That said, A and D both focus on changing the model itself, which doesn’t really fit how RAG works. I’d rule those out. C talks about text generation but misses the retrieval part entirely. So B still seems like the best pick because it points to combining retrieval with generation, which is what sets RAG apart.

Pretty sure it’s B. The key point is that RAG isn’t just about generating text but actually pulling in relevant info from outside sources to help the generation. A and D talk about retraining or fine-tuning, which is a different setup. C sounds too general and misses the retrieval aspect, so B captures the combo best.

B/C? B fits well since it mentions combining retrieval with generation, but C also talks about generation with Transformers. Still, C doesn’t highlight the retrieval part, which is crucial for RAG. So B wins for me.

This one feels like B since RAG specifically uses retrieval to improve the generated response, not just retraining or general Transformer use. So B makes the most sense here.

B. RAG definitely mixes retrieving info with generating answers, so it’s not just about using Transformers or retraining the model. The retrieval part is what sets it apart from usual generation methods.

Makes sense to rule out A and D since those focus on retraining, which RAG doesn’t do. B is the only one mentioning retrieval plus generation together, so I’d pick B too.

Probably B here. The key is that RAG involves pulling in external info during generation, not just tweaking the model or using Transformers in general. A and D focus more on retraining or fine-tuning, which is different from what RAG actually does. C is just about text generation with Transformers but doesn’t mention retrieval. So B stands out because it specifically combines retrieval and generation steps, which matches the core idea behind RAG.

B/C? B fits because it explicitly says combining retrieval and generation, which is what RAG is about. C sounds too general about Transformers without the retrieval aspect.

Pretty sure the answer is B. RAG mixes retrieval with generation to improve responses, not just fine-tuning or retraining.