AIF-C01 Applications of Foundation Models Practice Question
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?
⚠ Common exam trap
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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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 list content to avoid but do not instruct the model what relevant material to include, so irrelevant output persists. They are tempting because they target unwanted content, and would be correct for suppressing specific phrases or topics, not for improving summary relevance.
- ✓
Use few-shot examples with summaries
Why this is correct
Few-shot examples supply the model with concrete input-output pairs demonstrating the desired summary style and length, steering generation toward relevant content. This conditioning is more effective than abstract instructions because the model infers the expected pattern directly from demonstrated summaries.
- ✗
Increase max tokens
Why it's wrong here
Raising max tokens only extends the generation ceiling; it cannot remove irrelevant content, and may lengthen output. It is genuinely useful when responses are truncated mid-sentence by the token limit, which is not this scenario.
- ✗
Decrease temperature
Why it's wrong here
Lowering temperature makes token selection more deterministic, but relevance depends on prompt content and instructions, not sampling randomness. Temperature reduction is the right lever when outputs must be consistent and factual, not when scope must be narrowed.
Go deeper
Related to this question
About these practice questions
This AIF-C01 question is part of Courseiva's 862-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.