NCA-GENL Software Development Practice Question
When evaluating an LLM for a domain-specific task, why is 'Few-Shot Prompting' often superior to 'Zero-Shot Prompting'?
⚠ Common exam trap
Candidates often believe few-shot prompting modifies the underlying model weights permanently, confusing it with parameter fine-tuning.
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
✓
It provides in-context learning examples to guide the model.
Few-shot prompting provides the model with concrete examples of the desired input-output mapping, which acts as a guide for structure and reasoning. This reduces ambiguity and aligns the model's output with application-specific requirements. It is a fundamental technique for improving model precision in specialized domains where the model needs to understand unique formatting or logical patterns that are not explicitly defined in its base training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It decreases the number of tokens processed per request.
Why it's wrong here
Few-shot prompting actually increases the token count because it includes additional examples in the prompt. This can increase latency slightly, but the trade-off is higher accuracy and better formatting. The primary benefit is not efficiency, but performance and behavioral alignment through contextual demonstration.
- ✓
It provides in-context learning examples to guide the model.
Why this is correct
By presenting examples (shots) within the prompt, the model uses them to understand the pattern or task structure. This in-context learning is highly effective for specialized tasks where the model needs to adapt to specific user requirements or output formats without requiring expensive fine-tuning.
- ✗
It forces the model to ignore its internal pre-trained knowledge.
Why it's wrong here
Few-shot prompting complements internal knowledge rather than ignoring it. It provides a frame of reference for how that internal knowledge should be applied. The model still uses its broad training, but the examples guide the model's focus to ensure the output meets specific user expectations.
- ✗
It is the only method to ensure the model produces non-hallucinated results.
Why it's wrong here
While few-shot prompting improves reliability, it is not a silver bullet for hallucinations. Grounding in verified data (RAG) is far more effective for factual accuracy. Few-shot is primarily for format and task alignment, not for replacing the factual grounding required to prevent model hallucinations.
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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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