1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
An enterprise RAG application experiences high latency during peak hours. The architecture uses OCI OpenSearch with a single node cluster storing 5 million vectors (768 dimensions). The search uses exact k-NN (EF_SEARCH=500). The average query takes 1.5 seconds, but the SLA requires <500ms. The team considers several options: A) Switch to ANN with lower recall (HNSW with ef_search=50), B) Scale OpenSearch cluster to 3 nodes, C) Reduce embedding dimension to 256 using PCA, D) Increase the number of shards from 1 to 10. Which option provides the best balance of latency reduction and minimal impact on retrieval quality? (Assume all options are feasible)
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
✓
Increase the number of shards from 1 to 10
Increasing shards on the same node partitions the index, so each shard contains fewer vectors, making exact search faster. This reduces latency without sacrificing accuracy. ANN reduces recall, scaling adds cost and complexity, and dimension reduction can degrade embedding quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale OpenSearch cluster to 3 nodes
Why it's wrong here
Improves throughput but per-query latency remains high due to exact search on each node.
- ✓
Increase the number of shards from 1 to 10
Why this is correct
More shards divide the vector set, allowing parallel exact searches on smaller partitions, reducing latency without quality loss.
- ✗
Switch to ANN (HNSW with ef_search=50)
Why it's wrong here
ANN may miss relevant results, reducing retrieval quality.
- ✗
Reduce embedding dimension to 256 using PCA
Why it's wrong here
Dimensionality reduction may lose semantic information, harming retrieval quality.
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