1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A document processing pipeline uses OCI Document Understanding to extract text from PDFs, then creates embeddings with OCI Generative AI. Some documents exceed the embedding model's token limit. What is the best approach?
⚠ Common exam trap
A common mix-up: candidates assume truncation (Option A) is acceptable because it's simple, but they overlook the critical loss of information that undermines retrieval accuracy in RAG systems.
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
✓
Split the document into chunks that fit the limit and embed each chunk separately
Splitting documents into chunks that fit within the embedding model's token limit ensures that no information is lost while still allowing each chunk to be embedded and indexed separately. This approach is standard in RAG pipelines, where documents are chunked to balance token limits and retrieval granularity, enabling the system to retrieve relevant chunks rather than entire documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Truncate the document to the token limit
Why it's wrong here
Truncation loses potentially important content.
- ✗
Use a different embedding model with a higher token limit
Why it's wrong here
Not always available or may not be suitable for the domain.
- ✗
Skip documents that exceed the limit
Why it's wrong here
Skipping leads to information loss.
- ✓
Split the document into chunks that fit the limit and embed each chunk separately
Why this is correct
Chunking preserves full content and allows granular retrieval.
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