1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which TWO of the following are valid approaches to serve a RAG application in OCI with low latency?
⚠ Common exam trap
Oracle often tests the misconception that increasing chunk size or pre-computing all answers are viable latency solutions, when in fact they introduce precision loss or impracticality, while caching and streaming are the architecturally sound approaches for low-latency RAG serving.
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 a caching layer for frequently asked questions.
Implementing a caching layer for frequently asked questions reduces redundant LLM invocations and vector store queries, directly lowering latency for repeated queries. This approach leverages in-memory caches like Redis or Memcached to serve precomputed responses, bypassing the retrieval and generation pipeline for cached items.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-compute embeddings and answers for all possible questions.
Why it's wrong here
This is not scalable or realistic due to infinite question variations.
- ✗
Deploy the vector store on multiple regions to reduce network latency.
Why it's wrong here
Multi-region deployment adds complexity and consistency issues; typically not needed for low latency if close to LLM.
- ✗
Increase the chunk size to reduce the number of retrievals.
Why it's wrong here
Larger chunks may reduce retrieval count but increase LLM processing time due to larger context.
- ✓
Implement a caching layer for frequently asked questions.
Why this is correct
Caching avoids redundant retrieval and generation, reducing latency for common queries.
- ✓
Use an LLM that supports streaming response for faster user feedback.
Why this is correct
Streaming allows the user to see partial results, improving perceived latency.
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