hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A company has built a RAG application using…
A company has built a RAG application using Amazon Bedrock Knowledge Bases. Users report that answers are sometimes based on irrelevant or incorrect document chunks. The team has verified that the embedding model is appropriate and the documents are correctly indexed. What is the MOST likely cause of the poor retrieval quality?
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
✓
The chunking strategy is suboptimal
Chunking strategy directly affects retrieval relevance. If chunks are too large, they may contain irrelevant information; if too small, they may miss context. Overlap size also matters. Optimizing chunking often fixes relevance issues when embeddings are correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The foundation model is too small
Why it's wrong here
The foundation model generates the answer, but retrieval quality depends on the chunks retrieved, not the model size.
- ✗
The prompt template is missing instructions
Why it's wrong here
Prompt instructions affect how the model uses retrieved chunks but not which chunks are retrieved.
- ✗
The vector store is too slow
Why it's wrong here
Speed affects latency, not relevance. Irrelevant results indicate a retrieval quality issue, not a performance issue.
- ✓
The chunking strategy is suboptimal
Why this is correct
Chunking size, overlap, and strategy heavily impact which chunks are retrieved. Incorrect chunking can lead to irrelevant results even with good embeddings.
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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.