The correct choice is to improve the relevance of retrieved documents by refining the retrieval strategy, because hallucinations in a RAG system occur when the model lacks accurate, context-specific facts from the knowledge base. By enhancing retrieval relevance, you ensure the model receives precise, high-quality chunks that directly answer the query, reducing its need to rely on flawed parametric knowledge. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your understanding of how retrieval quality directly impacts model fidelity—a common trap is assuming larger chunk sizes or more context always help, when in fact they can introduce noise and worsen hallucinations. Remember, fine-tuning alone won’t fix missing facts; the retrieval step is the gatekeeper. Memory tip: Think “garbage in, garbage out”—better retrieval relevance means cleaner input, which cuts hallucinations at the source.
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 company deployed a question-answering system using Amazon Bedrock with a knowledge base (RAG). Users report that the model often hallucinates facts not in the knowledge base. What is the most effective way to reduce hallucinations?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Improve the relevance of retrieved documents by refining the retrieval strategy
Improving retrieval relevance ensures that the model receives accurate and contextually relevant information, reducing its reliance on parametric knowledge. Increasing chunk size or context may include irrelevant data. Fine-tuning alone may not fix hallucination if the model still lacks specific facts.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Reduce the maximum context length to limit model input
Why it's wrong here
Shrinking context may cut off necessary information, worsening responses.
✗
Fine-tune the foundation model on a large general corpus
Why it's wrong here
Fine-tuning on general data may not improve factuality for specific domains and could even introduce new errors.
✓
Improve the relevance of retrieved documents by refining the retrieval strategy
Why this is correct
Better retrieval ensures only pertinent information is provided, reducing the chance of hallucination.
Related concept
Static NAT maps one inside address to one outside address.
✗
Increase the chunk size of documents in the knowledge base
Why it's wrong here
Larger chunks may include more irrelevant information, potentially increasing confusion.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Similar concept trap
Larger chunks may include more irrelevant information, potentially increasing confusion.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Improve the relevance of retrieved documents by refining the retrieval strategy — Improving retrieval relevance ensures that the model receives accurate and contextually relevant information, reducing its reliance on parametric knowledge. Increasing chunk size or context may include irrelevant data. Fine-tuning alone may not fix hallucination if the model still lacks specific facts.
What should I do if I get this AIF-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO actions can help reduce the likelihood of hallucinations in a generative AI model used for question answering?
easy
A.Increase the maximum token count to allow more complete answers.
✓ B.Use Retrieval Augmented Generation (RAG) with a trusted knowledge base.
C.Fine-tune the model on the training data used for the application.
✓ D.Set a lower temperature parameter (e.g., 0.1) to reduce randomness.
E.Use a larger foundation model with more parameters.
Why B: Options A and C are correct. Grounding the model on a knowledge base (RAG) reduces hallucinations by providing factual context. Reducing the temperature parameter makes the model more deterministic, lowering the chance of making up information. Option B is wrong because fine-tuning on the same data that caused hallucinations may not fix the issue. Option D is wrong because increasing max tokens may allow more hallucinated content. Option E is wrong because using a larger model often increases hallucination risk due to more parameters.
Variation 2. A bank is using Amazon Bedrock to summarize customer support transcripts. The summaries often contain factual inaccuracies (hallucinations). Which approach is most effective for reducing hallucinations?
hard
A.Decrease the top-p to 0.1
B.Increase the model's temperature to make outputs more diverse
C.Fine-tune a smaller model on a large dataset of transcripts
✓ D.Implement RAG by grounding summarization on retrieved transcripts
Why D: Retrieval-Augmented Generation (RAG) grounds the model's output on retrieved transcripts, reducing the chance of fabricating details. Fine-tuning on transcripts may reinforce patterns but does not guarantee factual accuracy at inference time.
Last reviewed: Jun 23, 2026
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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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