1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which THREE factors should be considered when designing a vector search index for a RAG application that supports multiple languages?
⚠ Common exam trap
Oracle often tests the misconception that separate indexes per language are required for multilingual support, but the correct approach is to use a single index with a multilingual embedding model and language-specific preprocessing.
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
✓
Implement language identification as a preprocessing step.
Language identification as a preprocessing step ensures that documents are correctly tagged before indexing, which allows the system to apply appropriate language-specific tokenization, stop-word removal, and stemming. This prevents cross-language contamination in the vector index and improves retrieval accuracy for a multilingual RAG application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement language identification as a preprocessing step.
Why this is correct
Allows proper analyzer selection.
- ✗
Create separate vector indexes for each language.
Why it's wrong here
Usually not necessary; a single index with language field suffices.
- ✓
Use a multilingual embedding model that supports all required languages.
Why this is correct
Ensures cross-lingual semantic similarity.
- ✓
Configure language-specific text analyzers for preprocessing documents.
Why this is correct
Improves tokenization and stemming.
- ✗
Use larger chunk sizes for languages with complex morphology.
Why it's wrong here
Large chunks may lose nuance; optimal chunk size depends on content.
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