AI0-001 Implementing AI Solutions Practice Question
In prompt engineering, which technique involves providing a few correct input-output examples in the prompt to guide the model's response?
⚠ Common exam trap
AI0-001 often tests the confusion between few-shot (examples in the prompt) and chain-of-thought (step-by-step reasoning), so candidates must read whether the question emphasizes 'examples' or 'reasoning steps'.
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
✓
Few-shot prompting
Few-shot prompting provides a small number of input-output examples directly in the prompt so the model can infer the desired task format and pattern without any weight updates. This is distinct from zero-shot (no examples) and chain-of-thought (which elicits step-by-step reasoning rather than demonstrating examples).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
System prompt engineering
Why it's wrong here
System prompt engineering sets persistent behavioural instructions, not worked input-output pairs, so it cannot supply the exemplars the stem requires. It is tempting because system prompts genuinely shape tone, role and constraints across a conversation; that would be the right choice when a chatbot needs consistent persona or guardrails rather than few-shot demonstration.
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Chain-of-thought prompting
Why it's wrong here
Chain-of-thought prompting asks the model to expose intermediate reasoning steps, so it does not supply paired input-output examples as the stem requires. It is tempting because it genuinely improves arithmetic and multi-step logic tasks, where eliciting reasoning before the final answer is the correct technique.
- ✓
Few-shot prompting
Why this is correct
Few-shot prompting supplies a small number of worked input-output pairs within the prompt itself, letting the model infer the desired pattern and format. This differs from zero-shot, which gives instructions only, and from fine-tuning, which adjusts model weights.
- ✗
Zero-shot prompting
Why it's wrong here
Zero-shot prompting supplies no input-output examples, relying solely on the instruction, so it cannot satisfy the stem's requirement for correct example pairs. It is tempting because it suits straightforward tasks where the model already understands the format, such as simple classification or translation, making it the right choice when examples would waste context.
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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 CompTIA exam blueprint
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