- A
Increase the chunk_overlap in the text splitter
Why wrong: chunk_overlap affects document chunking, not the vector index's recall vs accuracy trade-off.
- B
Increase the 'neighbors' parameter
More neighbors per node improves recall by exploring more paths, but trades off accuracy (more false positives) and resource usage.
- C
Decrease the 'efConstruction' parameter
Why wrong: efConstruction controls search effort during construction; decreasing it reduces recall.
- D
Switch from HNSW to IVF index type
Why wrong: IVF may not improve recall; it is typically faster but less accurate than HNSW for many datasets.
Question 55 of 991
1Z0-1127 LangChain and AI Application Development Practice Question
This 1Z0-1127 practice question tests your understanding of langchain and ai application development. 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.
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?
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
In HNSW, the 'neighbors' parameter (often denoted M) sets the maximum number of connections per node in the hierarchical graph layers. A higher M value creates a denser graph with more redundant paths, which increases the probability that the search algorithm will find the true nearest neighbors, especially in high-dimensional spaces where the curse of dimensionality makes distance comparisons less reliable. In practice, doubling M from 16 to 32 can improve recall by several percentage points but may increase index size by roughly 50% and slow down queries by 10-20%.
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?
LangChain and AI Application Development — This question tests LangChain and AI Application Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
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
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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