1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which THREE are valid considerations when designing a RAG pipeline that uses OCI Generative AI and OCI OpenSearch? (Choose three.)
⚠ Common exam trap
Oracle often tests the misconception that vector databases only support one similarity metric (like Euclidean) or that documents must be stored as single vectors, when in practice they support multiple metrics and chunking is essential for effective retrieval.
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
✓
The quality of the text extraction from OCI Document Understanding directly impacts retrieval accuracy.
OCI Document Understanding performs text extraction from documents (e.g., PDFs, images). If the extraction is poor (e.g., missing text, OCR errors), the resulting chunks will be inaccurate, directly degrading the quality of vector embeddings and thus retrieval accuracy in the RAG pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI OpenSearch only supports Euclidean distance for vector similarity.
Why it's wrong here
OCI OpenSearch supports multiple distance metrics (cosine, Euclidean, etc.).
- ✗
Each document must be converted to a single vector for efficient retrieval.
Why it's wrong here
Documents are typically chunked into multiple vectors; a single vector loses granularity.
- ✓
The quality of the text extraction from OCI Document Understanding directly impacts retrieval accuracy.
Why this is correct
Poor extraction leads to noisy embeddings and irrelevant results.
- ✓
The generation model's context window size limits the number of chunks that can be included in the prompt.
Why this is correct
Exceeding the context window will cause truncation.
- ✓
The chunk size and overlap must be tuned based on the document type and query patterns.
Why this is correct
Proper chunking is essential for effective retrieval.
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