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AIF-C01 Practice Question: A machine learning team is using prompt…
A machine learning team is using prompt engineering to guide a large language model on Amazon Bedrock. They want the model to follow a specific reasoning process step-by-step. Which THREE prompt engineering techniques are most relevant? (Select THREE.)
⚠ Common exam trap
AWS often tests the distinction between techniques that guide reasoning (CoT, few-shot, system prompts) versus those that control output style (temperature), leading candidates to mistakenly select high temperature as a reasoning technique.
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 with examples of step-by-step reasoning
Option B (few-shot prompting with examples of step-by-step reasoning) is correct because providing worked examples that demonstrate each reasoning step conditions the model to imitate that structured reasoning pattern for new inputs. Option C (system prompts that describe the desired reasoning process) is correct because a system prompt sets persistent instructions and context for the model, so explicitly describing the required step-by-step reasoning process steers all subsequent responses accordingly. Option D (chain-of-thought prompting) is correct because CoT explicitly elicits intermediate reasoning steps before the final answer, which is exactly the step-by-step reasoning process the team wants. Option A (zero-shot prompting) does not belong because it only supplies a task instruction with no reasoning examples or process guidance, so it does not specifically enforce a step-by-step reasoning procedure. Option E (adjusting temperature to a high value) does not belong because temperature controls randomness/creativity of sampling, not the structure or presence of a reasoning process, and a high value would typically make outputs less deterministic rather than more step-by-step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Zero-shot prompting
Why it's wrong here
Zero-shot prompting supplies only an instruction with no worked examples, so it provides no scaffold for a step-by-step reasoning process. It is tempting because it is the simplest baseline and works for straightforward tasks, but chain-of-thought, few-shot and structured decomposition are what elicit explicit reasoning steps.
- ✓
Few-shot prompting with examples of step-by-step reasoning
Why this is correct
Few-shot prompting supplies worked examples demonstrating the exact step-by-step reasoning chain, letting the model imitate that structure through in-context learning. This directly satisfies the stem's goal of guiding the model to follow a specific reasoning process rather than producing an unstructured answer.
- ✓
System prompts that describe the desired reasoning process
Why this is correct
System prompts set persistent instructions that frame the entire interaction, so describing the desired reasoning process there steers every response toward that structure. This satisfies the stem's requirement by enforcing the step-by-step approach at the conversation level rather than per-example.
- ✓
Chain-of-thought (CoT) prompting
Why this is correct
Chain-of-thought prompting instructs the model to externalise intermediate reasoning steps before giving a final answer, which is precisely the step-by-step process the stem requires. It is the technique most directly aligned with eliciting structured, sequential reasoning from the model.
- ✗
Adjusting the temperature to a high value
Why it's wrong here
Temperature governs sampling randomness, not the structure of reasoning, so raising it produces more varied wording rather than a defined step-by-step process. It is tempting because generation parameters sit alongside prompt techniques, and high temperature is correct when brainstorming or diverse creative output is wanted.
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