1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A team is designing a RAG system for a multilingual knowledge base. Which TWO strategies are appropriate? (Choose two.)
⚠ Common exam trap
In Oracle OCI GenAI, a common misconception is that a single multilingual embedding model alone is sufficient for all multilingual RAG scenarios, but the correct answer pair (A and E) highlights that both a unified model and language-specific indices can be appropriate strategies depending on the system's requirements.
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
✓
Store separate vector indices per language
Storing separate vector indices per language allows the RAG system to optimize retrieval for each language's unique semantic and syntactic characteristics. This avoids cross-language interference and enables the use of language-specific preprocessing, tokenization, and embedding models, which improves retrieval accuracy for multilingual queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store separate vector indices per language
Why this is correct
Separate indices allow language-specific preprocessing and retrieval optimizations.
- ✗
Disable vector search for non-English queries
Why it's wrong here
This defeats the purpose of a multilingual system.
- ✗
Translate all documents to English before indexing
Why it's wrong here
Translation introduces latency and potential loss of meaning.
- ✗
Use a different embedding model per language
Why it's wrong here
Managing multiple models increases complexity and may cause embedding incompatibility.
- ✓
Use a single embedding model trained for multilingual text
Why this is correct
Multilingual models can embed documents and queries in various languages into a common space.
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