1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A data scientist is building a RAG application that processes PDF invoices. The extraction step uses OCI Document Understanding to convert PDFs to text. The scientist then splits the text into chunks and generates embeddings using OCI Generative AI. However, the retrieval often misses critical fields like invoice numbers and dates. Which preprocessing step would MOST likely improve retrieval of these specific fields?
⚠ Common exam trap
Oracle often tests the misconception that increasing chunk size or changing embedding type alone can solve retrieval failures for structured fields, when in reality metadata filtering is the correct technique for precise field-level retrieval in RAG applications.
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
✓
Tag each chunk with metadata such as invoice number, date, and vendor, and use metadata filtering during retrieval.
Metadata tagging and filtering directly address the retrieval of specific fields like invoice numbers and dates. By attaching metadata (e.g., invoice number, date, vendor) to each chunk and filtering on these metadata fields during retrieval, the RAG system can precisely locate the relevant chunks without relying solely on semantic similarity. This approach leverages OCI Document Understanding's ability to extract structured data and OCI Generative AI's vector search capabilities to combine dense embeddings with exact metadata matching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the chunk size to include entire invoices.
Why it's wrong here
Larger chunks may reduce the density of key information per chunk.
- ✗
Apply stemming and lemmatization to the text before chunking.
Why it's wrong here
Stemming does not significantly improve retrieval of specific fields.
- ✓
Tag each chunk with metadata such as invoice number, date, and vendor, and use metadata filtering during retrieval.
Why this is correct
Metadata filtering enables precise retrieval based on structured fields.
- ✗
Switch from dense embeddings to sparse embeddings for better exact match.
Why it's wrong here
Sparse embeddings may not capture semantic similarity effectively.
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