- A
Vertex AI AutoML
Why wrong: AutoML automates model training, not vector search.
- B
Vertex AI Workbench
Why wrong: Workbench is a Jupyter-based development environment.
- C
Vertex AI Prediction
Why wrong: Prediction serves models, not ANN indexes.
- D
Vertex AI Matching Engine (Vector Search)
Matching Engine (Vector Search) is for ANN similarity search on embeddings.
Question 250 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.
Which Vertex AI service is designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale?
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
✓
Vertex AI Matching Engine (Vector Search)
Vertex AI Matching Engine (now Vector Search) provides ANN indexes for similarity search, enabling fast vector similarity queries at scale.
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.
- ✗
Vertex AI AutoML
Why it's wrong here
AutoML automates model training, not vector search.
- ✗
Vertex AI Workbench
Why it's wrong here
Workbench is a Jupyter-based development environment.
- ✗
Vertex AI Prediction
Why it's wrong here
Prediction serves models, not ANN indexes.
- ✓
Vertex AI Matching Engine (Vector Search)
Why this is correct
Matching Engine (Vector Search) is for ANN similarity search on embeddings.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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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Serving and Scaling Models — study guide chapter
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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: Vertex AI Matching Engine (Vector Search) — Vertex AI Matching Engine (now Vector Search) provides ANN indexes for similarity search, enabling fast vector similarity queries at scale.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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