hardMultiple Choice
AIF-C01 Practice Question: Building a RAG application with Amazon Bedrock…
A company is building a RAG application with Amazon Bedrock Knowledge Bases. They want to ensure that the retriever returns the most semantically relevant chunks. They are using a large document corpus with many similar passages. Which chunking strategy is MOST likely to improve retrieval accuracy?
⚠ Common exam trap
A common misconception is that more granularity (smaller chunks) always improves retrieval accuracy, but the trap here is that overly small chunks lose context and semantic completeness, which actually degrades relevance in a RAG system.
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
✓
Semantic chunking that splits at paragraph or section boundaries
Semantic chunking splits documents at natural boundaries like paragraphs or sections, preserving the coherence of each chunk. This ensures that each chunk contains a complete, self-contained idea, which allows the retriever to match the semantic meaning of the query more accurately. In a corpus with many similar passages, this approach reduces noise and improves the relevance of retrieved chunks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fixed‑size token chunking with no overlap
Why it's wrong here
Fixed-size chunking without overlap severs sentences and context at token boundaries, so semantically similar passages lose distinguishing context and retrieval accuracy drops. It is tempting because fixed-size chunking is simple and predictable for uniform text, and would be correct for a corpus of short, self-contained documents where boundaries rarely split meaning.
- ✗
Overlapping fixed‑size chunks with 50% overlap
Why it's wrong here
Overlapping fixed-size chunks still split on token counts rather than semantic or structural boundaries, so similar passages remain ambiguous to the retriever. It is tempting because overlap preserves some context across boundaries, and would be correct when documents are homogeneous and no natural section structure exists to chunk along.
- ✓
Semantic chunking that splits at paragraph or section boundaries
Why this is correct
Semantic chunking splits text where meaning shifts, using embedding similarity between adjacent sentences to detect paragraph or section boundaries. This preserves coherent, self-contained passages, so the retriever's vector comparison distinguishes between the many similar passages in the corpus, directly improving semantic relevance of returned chunks.
- ✗
Very small chunks (50 tokens) to maximize granularity
Why it's wrong here
Fifty-token chunks fragment passages into incomplete statements, stripping the surrounding context the retriever needs to discriminate between similar passages. It is tempting because small chunks raise granularity and embedding precision, and would be correct for keyword-style lookup over short, independent records rather than prose.
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