Quick Answer
The answer is to use few-shot examples with summaries, as this technique most effectively improves relevance by providing the model with explicit patterns of desired output. Few-shot prompting works by embedding a small set of high-quality input-output pairs directly into the prompt, giving the model concrete reference points that guide it to match both the format and content of those examples, thereby reducing the inclusion of irrelevant information. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how to apply prompt engineering within Amazon Bedrock to control generative AI behavior, often appearing as a scenario where a developer needs to refine summary quality. A common trap is choosing zero-shot instructions or chain-of-thought reasoning, which are better for reasoning tasks rather than output relevance. Remember the memory tip: “Show, don’t just tell”—few-shot examples show the model exactly what a relevant summary looks like, making them the most direct path to improving relevance.
This AIF-C01 practice question tests your understanding of applications of 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 developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?
Add a negative prompt specifying what to avoid
Why wrong: Negative prompts can reduce irrelevant content but are not as effective as few-shot for relevance.
Use few-shot examples with summaries
Few-shot examples show the model desired output patterns, improving relevance.
Increase max tokens
Why wrong: Max tokens control length, not relevance.
Decrease temperature
Why wrong: Temperature controls randomness, not relevance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use few-shot examples with summaries
Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Add a negative prompt specifying what to avoid
Why it's wrong here
Negative prompts can reduce irrelevant content but are not as effective as few-shot for relevance.
Use few-shot examples with summaries
Why this is correct
Few-shot examples show the model desired output patterns, improving relevance.
Related concept
Read the scenario before looking for a memorised answer.
Increase max tokens
Why it's wrong here
Max tokens control length, not relevance.
Decrease temperature
Why it's wrong here
Temperature controls randomness, not relevance.
Common exam traps
AWS often tests the misconception that adjusting generation parameters (like temperature or token limits) can substitute for explicit prompt structure, when in fact few-shot examples directly teach the model the expected output format and content relevance.
Detailed technical explanation
Few-shot prompting leverages in-context learning, where the model uses the provided examples to infer the task structure without fine-tuning. For summarization, including 2–3 high-quality examples of relevant summaries helps the model align its output distribution with the desired pattern, effectively narrowing the generation space. This technique is especially valuable when the model's pre-training data includes diverse summarization styles, as the examples act as a filter for relevance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: Use few-shot examples with summaries — Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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