1Z0-1127-25 LangChain and AI Application Development Practice Question
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?
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 default parameters
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Exact nearest neighbor search without an index
Why it's wrong here
Exact search is prohibitively slow for large datasets and is not an index type.
- ✓
HNSW index with default parameters
Why this is correct
HNSW typically provides high recall and moderate indexing time compared to exact search, making it suitable for production semantic search.
- ✗
No index — rely on full table scan
Why it's wrong here
Full table scan is extremely slow and does not scale; an index is necessary for performance.
- ✗
IVF index with a large number of centroids
Why it's wrong here
IVF indexing is faster but recall may degrade; large centroids improve recall but increase search time, and indexing time may still be moderate.
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