- A
Brute-force index
Why wrong: Brute-force is exact but slow for large datasets; latency would exceed 10ms.
- B
Hash-based index
Why wrong: Hash-based indexes are not supported in Vertex AI Vector Search.
- C
Tree-based index
Why wrong: Tree-based indexes are not used by Vertex AI Vector Search; it uses ScaNN.
- D
Approximate nearest neighbor (ANN) index with ScaNN
ANN with ScaNN provides fast approximate search, suitable for large-scale real-time search.
Question 47 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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 company needs to perform real-time similarity search on a dataset of 10 million embedding vectors. They expect low latency (under 10ms) and high throughput. Which index type should they use in Vertex AI Vector Search?
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
✓
Approximate nearest neighbor (ANN) index with ScaNN
For large datasets requiring low latency, an approximate nearest neighbor (ANN) index is appropriate. The Scann algorithm (ScaNN) is used by Vertex AI Vector Search for ANN.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Brute-force index
Why it's wrong here
Brute-force is exact but slow for large datasets; latency would exceed 10ms.
- ✗
Hash-based index
Why it's wrong here
Hash-based indexes are not supported in Vertex AI Vector Search.
- ✗
Tree-based index
Why it's wrong here
Tree-based indexes are not used by Vertex AI Vector Search; it uses ScaNN.
- ✓
Approximate nearest neighbor (ANN) index with ScaNN
Why this is correct
ANN with ScaNN provides fast approximate search, suitable for large-scale real-time search.
Related concept
OSPF neighbours must agree on key parameters.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related PMLE OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Approximate nearest neighbor (ANN) index with ScaNN — For large datasets requiring low latency, an approximate nearest neighbor (ANN) index is appropriate. The Scann algorithm (ScaNN) is used by Vertex AI Vector Search for ANN.
What should I do if I get this PMLE question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related PMLE OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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