mediumMultiple SelectObjective-mapped
AIF-C01 Practice Question: Using Amazon Bedrock Knowledge Bases with a RAG…
A company is using Amazon Bedrock Knowledge Bases with a RAG pipeline. They want to improve the relevance of retrieved chunks for user queries. Which TWO configuration changes are likely to help?
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
✓
Use a model with higher-dimensional embeddings
Reducing chunk size can improve precision, and using embeddings with higher dimensions may capture more semantic nuance. Increasing chunk size typically reduces precision, and disabling chunking would break retrieval. Increasing max context length does not affect retrieval.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a model with higher-dimensional embeddings
Why this is correct
Higher dimensions can capture finer semantic differences.
- ✗
Disable chunking and store entire documents as single vectors
Why it's wrong here
Storing whole documents loses granularity and harms retrieval relevance.
- ✓
Reduce the chunk size
Why this is correct
Smaller chunks can retrieve more specific, relevant content.
- ✗
Increase the model's max context length
Why it's wrong here
Context length does not impact retrieval quality.
- ✗
Increase the chunk size
Why it's wrong here
Larger chunks may include irrelevant information, reducing precision.
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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.