- A
Increase the max tokens to allow longer summaries
Why wrong: Longer summaries do not reduce hallucinations; they may increase them.
- B
Use few-shot prompting with examples that demonstrate accurate summaries
Providing examples of correct summaries guides the model.
- C
Increase the temperature to 0.9 for more creative summaries
Why wrong: Higher temperature increases randomness and may increase hallucinations.
- D
Use chain-of-thought prompting to encourage the model to reason step-by-step
Chain-of-thought can help the model verify facts before outputting.
- E
Lower the temperature to a value closer to 0.0
Lower temperature makes outputs more deterministic, reducing hallucination risk.
Question 513 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 data scientist is using a pre-trained LLM for a text summarization task. They notice the model sometimes includes hallucinations (false information) in the summaries. Which THREE prompt engineering techniques can help reduce hallucinations? (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
✓
Use few-shot prompting with examples that demonstrate accurate summaries
Few-shot prompting (B) provides the model with concrete examples of accurate summaries, establishing a pattern that reduces the likelihood of generating fabricated information. By conditioning the model on high-quality demonstrations, it learns to mimic the factual style and structure, directly mitigating hallucinations.
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.
- ✗
Increase the max tokens to allow longer summaries
Why it's wrong here
Longer summaries do not reduce hallucinations; they may increase them.
- ✓
Use few-shot prompting with examples that demonstrate accurate summaries
Why this is correct
Providing examples of correct summaries guides the model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the temperature to 0.9 for more creative summaries
Why it's wrong here
Higher temperature increases randomness and may increase hallucinations.
- ✓
Use chain-of-thought prompting to encourage the model to reason step-by-step
Why this is correct
Chain-of-thought can help the model verify facts before outputting.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Lower the temperature to a value closer to 0.0
Why this is correct
Lower temperature makes outputs more deterministic, reducing hallucination risk.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS AI Practitioner exam often tests the misconception that increasing creativity (high temperature) or output length (max tokens) can improve summary quality, when in fact these parameters increase hallucination risk, while low temperature and few-shot examples are the correct mitigations.
Detailed technical explanation
How to think about this question
Temperature controls the softmax distribution's sharpness: at 0.0, the model always selects the most likely token (greedy decoding), which minimizes variance but can still hallucinate if the training data contains inaccuracies. Few-shot prompting works by providing in-context examples that bias the model's attention toward factual patterns, effectively narrowing the output distribution toward verified content. In practice, combining low temperature with few-shot examples is a common strategy for production summarization systems to balance coherence and factuality.
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
Got this wrong? Here's your next step.
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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: Use few-shot prompting with examples that demonstrate accurate summaries — Few-shot prompting (B) provides the model with concrete examples of accurate summaries, establishing a pattern that reduces the likelihood of generating fabricated information. By conditioning the model on high-quality demonstrations, it learns to mimic the factual style and structure, directly mitigating hallucinations.
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
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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