hardMultiple Choice
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
Foundation model size affects generation quality, not which chunks the retriever returns; irrelevant chunks are selected during vector search regardless of model capacity. A larger model is tempting because it improves reasoning over supplied context, and would be correct if retrieval were accurate but synthesis weak.
- ✗
The prompt template is missing instructions
Why it's wrong here
Retrieval happens before the prompt is assembled, so template wording cannot change which chunks the vector search returns; irrelevant chunks are already selected. Prompt instructions are tempting because they govern how the model uses retrieved context, and would be the fix if correct chunks were retrieved but answered poorly.
- ✗
The vector store is too slow
Why it's wrong here
Vector store latency affects response time, not retrieval relevance; slow queries still return the same nearest-neighbour chunks. Performance tuning is tempting because latency is a common operational complaint, and would be the right focus if the issue were slow responses rather than incorrect source documents being matched.
- ✓
The chunking strategy is suboptimal
Why this is correct
Suboptimal chunking splits documents so that semantically related content is fragmented or unrelated text is merged, degrading embedding quality and returning irrelevant chunks. Since embeddings and indexing are verified correct, chunk boundaries are the remaining retrieval-quality cause.
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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.