hardMultiple Choice
AIF-C01 Practice Question: A company uses Amazon Bedrock with a Knowledge…
A company uses Amazon Bedrock with a Knowledge Base for RAG. Users report that the assistant gives incorrect answers for questions that require understanding of data tables. After reviewing, the team suspects the chunking strategy is breaking table structures. Which change would BEST preserve the integrity of tabular data?
⚠ Common exam trap
A common mix-up: candidates confuse vector store selection with chunking strategy, assuming a different database will magically fix data integrity issues, when in fact the chunking method directly controls how tabular data is preserved.
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
✓
Switch from fixed-size chunking to semantic chunking that respects table boundaries
Semantic chunking that respects table boundaries preserves the logical structure of tabular data by ensuring that rows, columns, and headers remain intact within a single chunk. Fixed-size chunking can split a table mid-row or mid-column, causing the knowledge base to retrieve incomplete or misaligned data, which leads to incorrect RAG answers. This approach directly addresses the root cause—broken table structures—without changing the vector store or overlap settings.
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 different vector store like Pinecone
Why it's wrong here
Swapping the vector store changes retrieval indexing, not how documents are split, so broken table structures remain broken. It is tempting because vector stores affect retrieval quality, but the stem identifies chunking as the cause. A structure-aware chunking strategy that keeps tables intact is what preserves tabular integrity.
- ✓
Switch from fixed-size chunking to semantic chunking that respects table boundaries
Why this is correct
Semantic chunking splits on meaning rather than character count, so table rows and headers stay within one chunk. Fixed-size splitting severs rows from headers, destroying the relationships the model needs, whereas boundary-aware chunking preserves tabular structure for retrieval.
- ✗
Increase the chunk overlap to 50%
Why it's wrong here
Overlap merely duplicates adjacent text; it cannot reconstruct a table split across chunk boundaries, so rows and headers still fragment. Overlap suits prose where context spans sentences. Preserving tabular integrity requires structure-aware chunking or a model that parses tables, not larger overlap.
- ✗
Decrease the chunk size to 100 tokens
Why it's wrong here
Smaller chunks fragment tables further, separating headers from rows and worsening the integrity problem the stem describes. It is tempting because smaller chunks can sharpen retrieval granularity, but that trade-off harms tabular coherence. Structure-aware chunking that keeps whole tables together is what preserves table semantics.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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