1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?
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
✓
Cache frequent queries and their embeddings
Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.
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 a larger generation model
Why it's wrong here
Larger generation models increase cost per generation.
- ✓
Cache frequent queries and their embeddings
Why this is correct
Caching reduces redundant embedding API calls, lowering costs.
- ✗
Reduce chunk size to decrease embedding calls
Why it's wrong here
Smaller chunks may increase the number of chunks and thus embedding calls.
- ✗
Use a larger embedding model for better accuracy
Why it's wrong here
Larger models cost more per API call.
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