- A
Reduce the maximum context length to limit model input
Why wrong: Shrinking context may cut off necessary information, worsening responses.
- B
Fine-tune the foundation model on a large general corpus
Why wrong: Fine-tuning on general data may not improve factuality for specific domains and could even introduce new errors.
- C
Improve the relevance of retrieved documents by refining the retrieval strategy
Better retrieval ensures only pertinent information is provided, reducing the chance of hallucination.
- D
Increase the chunk size of documents in the knowledge base
Why wrong: Larger chunks may include more irrelevant information, potentially increasing confusion.
Question 211 of 1,000
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 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
✓
Improve the relevance of retrieved documents by refining the retrieval strategy
Option C is correct because hallucinations in RAG systems often stem from the model receiving irrelevant or low-quality retrieved documents, which forces it to rely on its parametric knowledge rather than the provided context. By refining the retrieval strategy—such as improving embedding quality, adjusting chunk overlap, or using hybrid search—the system ensures the foundation model has the most relevant information to ground its answers, directly reducing the likelihood of fabricating facts.
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.
- ✗
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
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
A common misconception is that hallucinations are primarily a model training issue (fine-tuning or context length) rather than a retrieval quality issue in RAG systems, leading candidates to overlook the critical role of the retriever in grounding responses.
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
Under the hood, RAG systems use embeddings to retrieve chunks based on semantic similarity; if the retrieval strategy is suboptimal (e.g., using a single embedding model without reranking or query expansion), the top-k chunks may be only tangentially related. A real-world scenario is a legal Q&A system where a poorly tuned retriever returns chunks about 'contract termination' for a query about 'breach of contract,' causing the model to hallucinate incorrect legal remedies. Refining retrieval with techniques like Maximal Marginal Relevance (MMR) or hybrid keyword-vector search can dramatically improve the relevance of the context window.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Improve the relevance of retrieved documents by refining the retrieval strategy — Option C is correct because hallucinations in RAG systems often stem from the model receiving irrelevant or low-quality retrieved documents, which forces it to rely on its parametric knowledge rather than the provided context. By refining the retrieval strategy—such as improving embedding quality, adjusting chunk overlap, or using hybrid search—the system ensures the foundation model has the most relevant information to ground its answers, directly reducing the likelihood of fabricating facts.
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
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