- A
LSH (Locality Sensitive Hashing)
Why wrong: LSH is approximate and typically lower recall than HNSW.
- B
Flat (brute-force)
Why wrong: Flat gives exact results but is too slow for hundreds of millions of documents.
- C
HNSW (Hierarchical Navigable Small World)
HNSW offers a good balance of high recall and reasonable latency, suitable for large-scale vector search.
- D
IVF (Inverted File Index)
Why wrong: IVF is faster but lower recall in very large datasets.
Question 336 of 991
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector 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 data scientist is designing a RAG system with a large vector database (hundreds of millions of documents) and requires high recall accuracy. Which vector search index type should be used in OCI Search with OpenSearch?
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
✓
HNSW (Hierarchical Navigable Small World)
HNSW is the correct choice because it provides the best trade-off between high recall and low latency for large-scale vector search. It builds a multi-layer graph structure that enables efficient approximate nearest neighbor search, achieving recall accuracy close to brute-force while scaling to hundreds of millions of documents.
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.
- ✗
LSH (Locality Sensitive Hashing)
Why it's wrong here
LSH is approximate and typically lower recall than HNSW.
- ✗
Flat (brute-force)
Why it's wrong here
Flat gives exact results but is too slow for hundreds of millions of documents.
- ✓
HNSW (Hierarchical Navigable Small World)
Why this is correct
HNSW offers a good balance of high recall and reasonable latency, suitable for large-scale vector search.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
IVF (Inverted File Index)
Why it's wrong here
IVF is faster but lower recall in very large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Flat (brute-force) thinking it guarantees the highest recall, but they overlook the massive latency penalty at scale in OCI Search with OpenSearch. HNSW achieves near-perfect recall with orders-of-magnitude faster search and is the recommended index type for high-recall large-scale vector search in OCI.
Detailed technical explanation
How to think about this question
HNSW constructs a hierarchical graph where each layer is a navigable small-world graph, allowing search to start at the top layer (coarse) and descend to finer layers, dramatically reducing the number of distance computations. In OCI Search with OpenSearch, HNSW is implemented as the default engine for k-NN search, supporting dynamic indexing and real-time updates. A real-world scenario where HNSW excels is in e-commerce product similarity search, where high recall is critical for user satisfaction even with billions of embeddings.
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: HNSW (Hierarchical Navigable Small World) — HNSW is the correct choice because it provides the best trade-off between high recall and low latency for large-scale vector search. It builds a multi-layer graph structure that enables efficient approximate nearest neighbor search, achieving recall accuracy close to brute-force while scaling to hundreds of millions of documents.
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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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