Building LLM Applications with RAG and Vector Search →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

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

✓

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space

OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use OCI Document Understanding to convert images to text, then index text
Why it's wrong here
Loses visual information.
✗
Use separate vector stores for text and image embeddings
Why it's wrong here
Requires complex fusion logic and may not support cross-modal retrieval.
✗
Use image captioning to generate text descriptions and index those
Why it's wrong here
Captioning may miss details.
✓
Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space
Why this is correct
Multi-modal models enable direct retrieval of both types.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

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