1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?
⚠ Common exam trap
It's easy for candidates to confuse the HNSW 'neighbors' parameter with the 'efConstruction' parameter, mistakenly thinking that decreasing efConstruction (which speeds up construction) would improve recall, when in fact it reduces the thoroughness of graph building and lowers recall.
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 'neighbors' parameter
Increasing the 'neighbors' parameter (M) in an HNSW index expands the number of bidirectional links per node during graph construction. This denser graph provides more alternative paths during search, improving recall because the search is less likely to miss relevant vectors, but it also increases memory usage and can slightly degrade search speed due to more edges to traverse.
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 chunk_overlap in the text splitter
Why it's wrong here
chunk_overlap affects document chunking, not the vector index's recall vs accuracy trade-off.
- ✓
Increase the 'neighbors' parameter
Why this is correct
More neighbors per node improves recall by exploring more paths, but trades off accuracy (more false positives) and resource usage.
- ✗
Decrease the 'efConstruction' parameter
Why it's wrong here
efConstruction controls search effort during construction; decreasing it reduces recall.
- ✗
Switch from HNSW to IVF index type
Why it's wrong here
IVF may not improve recall; it is typically faster but less accurate than HNSW for many datasets.
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