1Z0-1127-25 LangChain and AI Application Development Practice Question
An organization uses Oracle AI Vector Search in Oracle Database 23ai to store embeddings for a LangChain RAG application. They need to perform similarity search with high recall and low latency for a large dataset (10M vectors). Which index configuration should they choose?
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 index with appropriate parameters (e.g., efConstruction=200, M=32)
HNSW (Hierarchical Navigable Small World) indexes provide high recall and low latency for approximate nearest neighbor search, especially on large datasets. IVF (Inverted File) is also an option but typically requires more tuning and may have lower recall at high speed. HNSW is generally preferred for production workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Binary quantization without an index
Why it's wrong here
Binary quantization reduces precision; without index, search is still slow.
- ✓
HNSW index with appropriate parameters (e.g., efConstruction=200, M=32)
Why this is correct
HNSW provides high recall and low latency, suitable for large datasets.
- ✗
No index, use brute-force distance computation
Why it's wrong here
Brute-force is O(n) and too slow for 10M vectors at query time.
- ✗
IVF index with 1000 centroids
Why it's wrong here
IVF can be fast but may have lower recall; HNSW is typically better for high recall.
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Variation 1. A company is using Oracle AI Vector Search in Oracle Database 23ai for semantic search over product descriptions. They need to create an index that supports approximate nearest neighbor search with high recall and moderate indexing time. Which index type and parameters should they choose?
medium- A.Exact nearest neighbor search without an index
- ✓ B.HNSW index with default parameters
- C.No index — rely on full table scan
- D.IVF index with a large number of centroids
Why B: HNSW (Hierarchical Navigable Small World) indexes offer high recall and faster search times, but building the index takes longer. IVF (Inverted File) indexes index faster but may have lower recall unless tuned. For high recall and moderate indexing time, HNSW is preferred because it provides better accuracy at the cost of longer build time.
JA
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