- A
Use approximate nearest neighbor (ANN) search with a lower recall setting
ANN speeds up search by sacrificing some recall, which can be mitigated by re-ranking.
- B
Increase the number of retrieved documents to 10
Why wrong: Retrieving more documents increases post-processing time.
- C
Move the vector store to a different region
Why wrong: Regional moves can add network latency, not reduce it.
- D
Switch to a larger embedding model for better accuracy
Why wrong: Larger models increase latency due to higher computational cost.
Question 244 of 991
Reduce RAG Latency with Approximate Nearest Neighbor Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A real-time customer support chatbot uses RAG with OCI Generative AI. The average response time is 5 seconds, which is too slow. The team identifies the vector search as the bottleneck. Which optimization would most reduce latency?
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
✓
Use approximate nearest neighbor (ANN) search with a lower recall setting
The vector search bottleneck causing high latency is best addressed by reducing the computational cost of the nearest neighbor search. Approximate Nearest Neighbor (ANN) search with a lower recall setting trades a small amount of accuracy for a significant reduction in search time, directly targeting the root cause of the 5-second response time. This optimization is specifically designed for latency-sensitive applications like real-time chatbots.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 approximate nearest neighbor (ANN) search with a lower recall setting
Why this is correct
ANN speeds up search by sacrificing some recall, which can be mitigated by re-ranking.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of retrieved documents to 10
Why it's wrong here
Retrieving more documents increases post-processing time.
- ✗
Move the vector store to a different region
Why it's wrong here
Regional moves can add network latency, not reduce it.
- ✗
Switch to a larger embedding model for better accuracy
Why it's wrong here
Larger models increase latency due to higher computational cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the trade-off between accuracy and latency in vector search, where candidates mistakenly think increasing resources (like document count or model size) will improve speed, rather than optimizing the search algorithm itself.
Detailed technical explanation
How to think about this question
ANN algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) work by partitioning the vector space and only searching a subset of the index. Lowering the recall setting (e.g., reducing the 'ef_search' parameter in HNSW) decreases the number of candidate vectors evaluated per query, directly reducing search time. In a real-world scenario, a chatbot using a 1M-vector index might see latency drop from 5 seconds to under 500ms with a 90% recall setting, which is often acceptable for customer support.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
What to study next
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use approximate nearest neighbor (ANN) search with a lower recall setting — The vector search bottleneck causing high latency is best addressed by reducing the computational cost of the nearest neighbor search. Approximate Nearest Neighbor (ANN) search with a lower recall setting trades a small amount of accuracy for a significant reduction in search time, directly targeting the root cause of the 5-second response time. This optimization is specifically designed for latency-sensitive applications like real-time chatbots.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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