- A
Increase the number of replicas in OpenSearch
Why wrong: Replicas improve availability and throughput, but each query still runs exact search.
- B
Shard the index by document type
Why wrong: Sharding distributes data but query latency depends on search algorithm, not sharding alone.
- C
Use approximate nearest neighbor (ANN) search instead of exact
ANN search is orders of magnitude faster than exact search for large datasets.
- D
Use a smaller embedding model
Why wrong: Smaller model may reduce computation but often degrades retrieval quality.
Question 274 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.
During load testing, the RAG application's response time increases significantly. The vector search is performed on millions of vectors. 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 instead of exact
Option C is correct because approximate nearest neighbor (ANN) search algorithms, such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index), trade a small amount of accuracy for a dramatic reduction in latency when searching over millions of vectors. In OpenSearch, ANN is implemented via the k-NN plugin and can reduce query latency from seconds to milliseconds by avoiding an exhaustive scan of all vectors.
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.
- ✗
Increase the number of replicas in OpenSearch
Why it's wrong here
Replicas improve availability and throughput, but each query still runs exact search.
- ✗
Shard the index by document type
Why it's wrong here
Sharding distributes data but query latency depends on search algorithm, not sharding alone.
- ✓
Use approximate nearest neighbor (ANN) search instead of exact
Why this is correct
ANN search is orders of magnitude faster than exact search for large datasets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller embedding model
Why it's wrong here
Smaller model may reduce computation but often degrades retrieval quality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that scaling infrastructure (replicas) or reducing data size (smaller model) is the primary solution for latency, when the fundamental algorithmic change from exact to approximate search is the most impactful optimization for large-scale vector search.
Detailed technical explanation
How to think about this question
ANN algorithms like HNSW build a multi-layer graph where each layer contains a subset of vectors, allowing the search to start at a coarse level and refine at finer levels, achieving O(log n) complexity instead of O(n). In OpenSearch, the k-NN plugin supports HNSW and IVF, and the 'ef_search' parameter controls the trade-off between recall and latency; tuning this parameter is critical for production workloads. A real-world scenario is a customer-facing chatbot where sub-second response times are required, and exact search over 10 million vectors would take >1 second, while ANN can achieve <100 ms with 95%+ recall.
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 instead of exact — Option C is correct because approximate nearest neighbor (ANN) search algorithms, such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index), trade a small amount of accuracy for a dramatic reduction in latency when searching over millions of vectors. In OpenSearch, ANN is implemented via the k-NN plugin and can reduce query latency from seconds to milliseconds by avoiding an exhaustive scan of all vectors.
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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