hardMultiple Choice
AIF-C01 Practice Question: A company runs a question-answering application…
A company runs a question-answering application on Amazon Bedrock that answers from a large knowledge base. Recently, users have reported that the model gives incomplete answers, often missing details from the middle of documents. The team suspects the chunking strategy is suboptimal. Which adjustment is MOST likely to improve completeness?
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 overlapping chunks so that boundaries do not cut off meaningful content
Overlapping chunks ensure that sentences or concepts that span chunk boundaries are not lost, improving retrieval completeness. Larger chunks may cause loss of precision, smaller chunks may lose context, and embedding model change is not directly related to chunking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the embedding model to a more powerful one
Why it's wrong here
Swapping the embedding model changes retrieval ranking, not how documents are segmented, so details lost at chunk boundaries remain absent. It is tempting because stronger embeddings improve semantic matching, which is the right fix when retrieval returns irrelevant chunks rather than incomplete ones.
- ✓
Use overlapping chunks so that boundaries do not cut off meaningful content
Why this is correct
Overlapping chunks repeat content across adjacent boundaries, so sentences or details split between chunks still appear intact in at least one chunk. This directly addresses incomplete answers caused by boundaries cutting off meaningful content from the middle of documents.
- ✗
Increase the chunk size to include more context per chunk
Why it's wrong here
Larger chunks dilute the embedding, so retrieval surfaces less relevant passages and the model still misses middle details. It is tempting because more context per chunk helps when answers span long passages, but the stem points to a chunking-strategy flaw rather than raw context volume.
- ✗
Decrease the chunk size to capture more granular details
Why it's wrong here
Smaller chunks fragment related details across boundaries, so retrieved passages omit surrounding context and answers stay incomplete. It is tempting because smaller chunks sharpen retrieval precision for pinpoint factual lookups, but here the missing middle details demand broader context.
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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.