Question 190 of 991

LangChain and AI Application Development →hardMultiple ChoiceObjective-mapped

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.

An organization using Oracle AI Vector Search for a RAG application wants to minimize latency for vector similarity searches on a dataset of 10 million vectors. Which index type and parameter combination is MOST likely to achieve the lowest latency while maintaining high recall?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
IVF with quantization and probes=5
Why wrong: Quantization may save memory but can reduce recall, and probes=5 is too low for a 10M dataset, likely resulting in poor recall.
B
HNSW with neighbors=32
Why wrong: While HNSW is fast, neighbors=32 is relatively low and may reduce recall; higher neighbors like 64 give better recall at marginal latency cost.
C
HNSW with neighbors=64
HNSW with a higher neighbor count provides excellent recall with minimal latency overhead, often outperforming IVF for large datasets.
D
IVF with probes=10
Why wrong: IVF generally has higher latency than HNSW for a given recall level; probes=10 may be insufficient for high recall.

Full breakdown with real-world context →

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 with neighbors=64

HNSW (Hierarchical Navigable Small World) graphs are designed for low-latency approximate nearest neighbor (ANN) search, and increasing the number of neighbors (efConstruction/efSearch) directly improves recall at the cost of memory. With 10 million vectors, HNSW with neighbors=64 provides a denser graph, reducing the number of hops during search and achieving lower latency than IVF-based methods, which require scanning multiple clusters. This combination offers the best trade-off for high recall and minimal latency in a RAG application.

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.

✗
IVF with quantization and probes=5
Why it's wrong here
Quantization may save memory but can reduce recall, and probes=5 is too low for a 10M dataset, likely resulting in poor recall.
✗
HNSW with neighbors=32
Why it's wrong here
While HNSW is fast, neighbors=32 is relatively low and may reduce recall; higher neighbors like 64 give better recall at marginal latency cost.
✓
HNSW with neighbors=64
Why this is correct
HNSW with a higher neighbor count provides excellent recall with minimal latency overhead, often outperforming IVF for large datasets.
Clue confirmation
The clue words "most likely", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
IVF with probes=10
Why it's wrong here
IVF generally has higher latency than HNSW for a given recall level; probes=10 may be insufficient for high recall.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the misconception that IVF with more probes always reduces latency, when in fact HNSW's graph-based approach inherently provides lower latency for large-scale vector search due to its logarithmic search complexity.

Detailed technical explanation

How to think about this question

HNSW builds a multi-layer graph where each layer is a navigable small world, and search starts at the top layer with the longest edges, descending to lower layers for finer granularity. The 'neighbors' parameter (M in construction, efSearch in query) controls the number of candidate connections per node; higher values increase graph density, reducing the average path length during search. For 10 million vectors, HNSW typically achieves sub-millisecond latency with >95% recall, while IVF requires tuning the number of probes and centroids (e.g., 4096 centroids) to balance latency and recall, often resulting in higher latency for comparable recall.

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: HNSW with neighbors=64 — HNSW (Hierarchical Navigable Small World) graphs are designed for low-latency approximate nearest neighbor (ANN) search, and increasing the number of neighbors (efConstruction/efSearch) directly improves recall at the cost of memory. With 10 million vectors, HNSW with neighbors=64 provides a denser graph, reducing the number of hops during search and achieving lower latency than IVF-based methods, which require scanning multiple clusters. This combination offers the best trade-off for high recall and minimal latency in a RAG application.

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.

Are there clue words in this question I should notice?

Yes — watch for: "most likely", "minimum / minimize". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

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

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