Free NVIDIA NCA-GENL Actual Exam Questions - Question 12 Discussion

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.

C vs D — detailed task descriptions help, but explicit intermediate steps in D seem stronger.

It’s D because chain-of-thought prompting explicitly guides the model to break down problems, unlike zero-shot or retrieval methods that don’t force stepwise reasoning. That structure is key for multi-step tasks.

Makes sense to rule out B since unrelated examples don’t help much. I’d go with D because breaking steps down explicitly is what really pushes multi-step reasoning forward. So, D.

It’s D for sure. The key is the explicit intermediate steps in chain-of-thought prompting, which really guide the model through the reasoning process. Retrieval-augmented generation (A) might add info, but without structured steps, it’s not as reliable for multi-step logic. Few-shot with unrelated examples (B) is basically noise for this, and zero-shot with just detailed descriptions (C) lacks that stepwise clarity. Breaking things down explicitly is what makes the difference here.

I’m skeptical about option B since unrelated examples won’t really guide the model through the specific reasoning steps needed. Does zero-shot with detailed tasks (C) offer enough structure, or is explicit step breakdown in D still key?

D seems right since explicitly breaking down steps helps track complex logic better.

B tbh doesn’t make much sense since unrelated examples won’t help the model understand the specific multi-step logic needed. D clearly guides the reasoning process better than any other choice.

It’s D since chain-of-thought prompting forces the model to explicitly lay out each step, which is way better for complex problems than zero-shot or unrelated few-shot examples that don’t guide reasoning.

It’s D for me too. The key with multi-step reasoning is guiding the model through the process, not just telling it what to do. Chain-of-thought prompting breaks the problem down and lets the model handle each piece in sequence. Options like B and C don’t provide that clear stepwise path, and A’s retrieval without context doesn’t really help with reasoning itself. So, D stands out as the best way to boost performance on these tasks by making the thinking visible.

Option C seems less effective since just detailed descriptions without examples might not guide the model through complex steps. Chain-of-thought (D) really stands out because it explicitly breaks down reasoning.

D makes the most sense here. Breaking down steps clearly helps the model think through problems better than just throwing unrelated examples or no context at all.