hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: Building a RAG application that indexes thousands…
A company is building a RAG application that indexes thousands of PDF documents. They notice that some documents are very long (hundreds of pages) and the vector search often returns irrelevant chunks. Which configuration change would MOST improve retrieval relevance?
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
✓
Adjust the chunk size and overlap to better capture context from the documents
Adjusting chunk size and overlap ensures that chunks contain coherent information. Increasing embedding dimension does not directly improve relevance, nor does changing the model size. Using a different vector store does not inherently fix chunking issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from Amazon OpenSearch Serverless to Pinecone
Why it's wrong here
Both vector stores are capable; the problem is likely chunking, not the vector store itself.
- ✗
Increase the embedding dimension from 1024 to 4096
Why it's wrong here
Larger dimensions do not guarantee better relevance and may increase latency and cost.
- ✗
Use a larger, more capable foundation model for response generation
Why it's wrong here
A larger model improves generation but does not fix retrieval relevance.
- ✓
Adjust the chunk size and overlap to better capture context from the documents
Why this is correct
Proper chunking ensures that each chunk contains complete, context-rich information, improving the relevance of retrieved passages.
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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.