- A
Zero-shot prompting
Why wrong: Zero-shot does not explicitly enforce step-by-step reasoning.
- B
Few-shot prompting with examples of step-by-step reasoning
Examples in the prompt can teach the model to reason step by step.
- C
System prompts that describe the desired reasoning process
System prompts can instruct the model to reason step by step.
- D
Chain-of-thought (CoT) prompting
CoT prompts the model to reason step by step.
- E
Adjusting the temperature to a high value
Why wrong: High temperature increases randomness, which may hinder consistent reasoning.
Question 261 of 1,000
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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 is correct because few-shot prompting with examples of step-by-step reasoning provides the model with explicit demonstrations of the desired chain of thought, which guides it to replicate that structured reasoning process. This technique is particularly effective for tasks requiring multi-step logic, as it leverages in-context learning to align the model's output with the provided examples.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 does not explicitly enforce step-by-step reasoning.
- ✓
Few-shot prompting with examples of step-by-step reasoning
Why this is correct
Examples in the prompt can teach the model to reason step by step.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
System prompts that describe the desired reasoning process
Why this is correct
System prompts can instruct the model to reason step by step.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Chain-of-thought (CoT) prompting
Why this is correct
CoT prompts the model to reason step by step.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adjusting the temperature to a high value
Why it's wrong here
High temperature increases randomness, which may hinder consistent reasoning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Chain-of-thought (CoT) prompting works by appending a sequence of intermediate reasoning steps to the prompt, often formatted as 'Step 1: ... Step 2: ...', which the model learns to continue. Few-shot CoT extends this by providing multiple complete examples, reinforcing the pattern through in-context learning. System prompts can also set a persistent instruction (e.g., 'You are a reasoning assistant that explains each step') that influences the model's behavior across all turns, reducing the need for repeated instructions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Few-shot prompting with examples of step-by-step reasoning — Option B is correct because few-shot prompting with examples of step-by-step reasoning provides the model with explicit demonstrations of the desired chain of thought, which guides it to replicate that structured reasoning process. This technique is particularly effective for tasks requiring multi-step logic, as it leverages in-context learning to align the model's output with the provided examples.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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