Free NVIDIA NCA-GENL Actual Exam Questions

Maybe D is out since few-shot learning isn’t about full fine-tuning on huge datasets. C doesn’t really fit either because hyperparameter optimization is a different process. Between A and B, B involves training from scratch which few-shot definitely doesn’t do. So yeah, A makes the most sense because it’s about showing examples directly in the prompt to guide the model, not retraining it.

B tbh, few-shot learning isn't about training from scratch or fine-tuning on large data. It’s more about giving the model a few examples within the prompt itself, so A fits best by process of elimination.

C imo, NeMo focuses on building and fine-tuning NLP models rather than deploying them at scale for inference. That’s why D feels like the better fit for production deployment.

A imo, DeepStream is more geared towards video analytics and streaming applications, so it doesn’t quite fit the NLP inference deployment scenario here. B (HuggingFace) offers great models and APIs but isn’t really a deployment framework optimized for high-performance production inference. Between C and D, D makes more sense because Triton is built specifically for serving models efficiently at scale in production. NeMo is great for developing and fine-tuning NLP models, but you’d likely still deploy them using something like Triton to get the performance needed in real-world environments.

Maybe D doesn’t make much sense since positional encoding doesn’t speed up processing. It’s definitely not about reducing dimensionality (C) or stopping overfitting (B), so A still feels right.

Probably A, since transformers lack any built-in sense of sequence order.

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.

Makes sense to pick D. Triton since it’s designed for serving models efficiently in production. Git and Pandas don’t handle deployment, and Falcon is just the model itself, not the deployment tool.

Falcon’s the model, but you need Triton to actually deploy it in production.

B for sure, comparing to human translations is the standard benchmark here.

I’m thinking C and D don’t really fit because tone and syntactic complexity are more subjective or secondary factors. The main goal’s usually to check how close the translation is to a trusted reference, so B sounds logical if we want a clear performance measure.

A/B? I get that tokenization is mainly splitting text into tokens (A), but sometimes people consider the whole step that maps tokens to numbers as part of tokenization in LLM pipelines, which would be B. Still, strictly speaking, tokenization itself is about splitting, not number conversion. So A fits better if we separate those steps. C and D are definitely out since they’re different preprocessing tasks.

A, since tokenization is fundamentally about chopping text into pieces before any number stuff.

Makes sense to rule out A, C, and D since they don’t really measure how well the model performs after fine-tuning. B fits best as it directly checks improvement on actual data. B

D imo, since model size and layers are fixed and don’t show performance. Training time isn’t a performance metric either, so validating accuracy is the best way to see if fine-tuning worked.

Option D—NeMo’s the only one focused on prompt engineering and fine-tuning.

NeMo's the only one that fits all three options, so D for me.

A. It’s more about creating a secure enclave in hardware, so software can run isolated from the rest of the system—definitely not about AI fairness or data integration stuff like B, C, or D.

Think it’s mostly about hardware-level security, so A fits better than D.

A imo, since A/B testing is about comparing real user impact, not model robustness or latency.

A, because it’s about comparing user outcomes between two model versions.

Actually, unrelated examples in B are unlikely to help with reasoning tasks here.

D. Chain-of-thought prompting stands out since it breaks down the problem step-by-step, which is exactly what multi-step reasoning needs. The others just don't provide that clear intermediate process.

Option D makes the most sense since chunking is about dividing text so retrieval steps handle it better, not rewriting or generating anything new like A or B suggest.

It’s D because chunking helps break info into parts that retrieval models can handle better.

Yeah, LangChain’s main job is definitely tying together different LLM parts and tools into a smooth flow, so C sounds right to me. It’s not about hardware or shrinking models. C

C makes sense since LangChain coordinates multiple tools, not hardware or model size.

Maybe D, since generative AI isn’t about just analyzing data but actually producing new stuff. Options B and C don’t fit because they focus on models, not generating content.

A. The key phrase is “generate new and original data,” which really nails what generative AI is about. B talks about generating models, but that’s more about automation in model building, not the AI creating actual new content. C and D focus on improving or analyzing existing stuff, which misses the creative aspect entirely. So A fits the common understanding best.