- A
Use a larger model
Why wrong: Larger models may still hallucinate; RAG is more effective.
- B
Increase temperature
Why wrong: Increasing temperature increases randomness, which can worsen hallucinations.
- C
Use retrieval augmented generation
RAG provides the model with relevant context from a knowledge base, improving factual accuracy.
- D
Decrease max tokens
Why wrong: Max tokens limits output length but does not address hallucination.
Question 169 of 1,000
Reducing Hallucinations with RAG in Amazon Bedrock
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 with the Claude model for text summarization. The output sometimes includes inaccurate information. What is the best practice to reduce hallucinations?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 retrieval augmented generation
Retrieval Augmented Generation (RAG) grounds the model's output in external, authoritative knowledge sources by retrieving relevant documents and injecting them into the prompt context. This directly reduces hallucinations because the model generates summaries based on factual retrieved data rather than relying solely on its parametric memory, which is the primary source of inaccuracies in text summarization tasks.
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.
- ✗
Use a larger model
Why it's wrong here
Larger models may still hallucinate; RAG is more effective.
- ✗
Increase temperature
Why it's wrong here
Increasing temperature increases randomness, which can worsen hallucinations.
- ✓
Use retrieval augmented generation
Why this is correct
RAG provides the model with relevant context from a knowledge base, improving factual accuracy.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease max tokens
Why it's wrong here
Max tokens limits output length but does not address hallucination.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that model size or output length adjustments are the primary levers for accuracy, when in fact grounding techniques like RAG are the standard solution for reducing hallucinations in production systems.
Trap categories for this question
Command / output trap
Max tokens limits output length but does not address hallucination.
Detailed technical explanation
How to think about this question
RAG works by embedding the user's query, performing a similarity search against a vector database of trusted documents, and prepending the top-k retrieved passages to the prompt before sending it to the model. This effectively constrains the model's generation to the provided context, leveraging the model's instruction-following capability rather than its stored knowledge. In practice, for summarization tasks, RAG ensures that every claim in the output can be traced back to a source document, which is critical in regulated industries like healthcare or finance where factual accuracy is mandatory.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use retrieval augmented generation — Retrieval Augmented Generation (RAG) grounds the model's output in external, authoritative knowledge sources by retrieving relevant documents and injecting them into the prompt context. This directly reduces hallucinations because the model generates summaries based on factual retrieved data rather than relying solely on its parametric memory, which is the primary source of inaccuracies in text summarization tasks.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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