Quick Answer
The answer is to use approximate nearest neighbor (ANN) search to reduce vector search latency. This is correct because ANN algorithms, such as HNSW or IVF, sacrifice a minor degree of accuracy to dramatically speed up query times by avoiding exhaustive comparisons across the entire vector dataset. In the context of the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of the fundamental trade-off between latency and precision in vector databases. A common trap is assuming that exact search (k-NN) is the best optimization for high latency, when in fact it exacerbates the problem by scanning every vector. Remember the memory tip: “ANN for speed, exact for need”—if your application can tolerate slight inaccuracy, ANN is the go-to solution for latency-sensitive workloads.
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 company uses OCI Generative AI to create embeddings for a vector search. They notice high latency in search queries. What is one possible optimization?
Decrease batch size for embedding creation
Why wrong: Batch size affects embedding creation, not search latency.
Use approximate nearest neighbor (ANN) search
Why wrong: ANN reduces search latency compared to exact search.
Use exact search for better accuracy
Exact search is slower; the optimization would be to use ANN.
Increase the embedding dimension
Why wrong: Higher dimensions increase computation and latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use exact search for better accuracy
Approximate nearest neighbor (ANN) search trades a slight reduction in accuracy for a significant speedup, addressing high latency.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Decrease batch size for embedding creation
Why it's wrong here
Batch size affects embedding creation, not search latency.
Use approximate nearest neighbor (ANN) search
Why it's wrong here
ANN reduces search latency compared to exact search.
Use exact search for better accuracy
Why this is correct
Exact search is slower; the optimization would be to use ANN.
Related concept
OSPF neighbours must agree on key parameters.
Increase the embedding dimension
Why it's wrong here
Higher dimensions increase computation and latency.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
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
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related 1Z0-1127 OSPF questions on adjacency and route selection.
Building LLM Applications with RAG and Vector Search — study guide chapter
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Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise 1Z0-1127 questions linked to Fundamentals of Large Language Models.
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Practise 1Z0-1127 questions linked to Building LLM Applications with RAG and Vector Search.
Practise 1Z0-1127 questions linked to Deploying and Managing Generative AI on OCI.
Practise 1Z0-1127 questions linked to 1Z0-1127 fundamentals.
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Practise 1Z0-1127 questions linked to 1Z0-1127 troubleshooting.
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FAQ
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — OSPF neighbours must agree on key parameters..
The correct answer is: Use exact search for better accuracy — Approximate nearest neighbor (ANN) search trades a slight reduction in accuracy for a significant speedup, addressing high latency.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related 1Z0-1127 OSPF questions on adjacency and route selection.
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 23, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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