- A
Simpler to implement than brute-force
Why wrong: Brute-force is simpler; ANN requires index tuning.
- B
Supports real-time updates without index rebuild
Why wrong: Both index types can support streaming updates; not exclusive to ANN.
- C
Lower query latency for large datasets
ANN trades off some accuracy for much faster search.
- D
Reduced memory footprint compared to brute-force
ANN indexes are compressed and use less memory.
- E
Guaranteed exact nearest neighbor results
Why wrong: ANN provides approximate results, not exact.
Question 475 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 TWO of the following are benefits of using Vertex AI Matching Engine (Vector Search) over a brute-force nearest neighbor search? (Choose 2)
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
✓
Lower query latency for large datasets
Vertex AI Matching Engine uses approximate nearest neighbor (ANN) algorithms like ScaNN (Scalable Nearest Neighbors) to index high-dimensional vectors. For large datasets, ANN dramatically reduces query latency by avoiding a full scan of all vectors, unlike brute-force search which must compute distances against every vector. This makes option C correct because ANN trades a negligible accuracy loss for orders-of-magnitude faster retrieval.
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.
- ✗
Simpler to implement than brute-force
Why it's wrong here
Brute-force is simpler; ANN requires index tuning.
- ✗
Supports real-time updates without index rebuild
Why it's wrong here
Both index types can support streaming updates; not exclusive to ANN.
- ✓
Lower query latency for large datasets
Why this is correct
ANN trades off some accuracy for much faster search.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Reduced memory footprint compared to brute-force
Why this is correct
ANN indexes are compressed and use less memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Guaranteed exact nearest neighbor results
Why it's wrong here
ANN provides approximate results, not exact.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that approximate nearest neighbor search provides exact results, but the trap here is that candidates confuse 'nearest neighbor' with 'exact nearest neighbor,' forgetting that ANN algorithms like ScaNN are inherently approximate.
Detailed technical explanation
How to think about this question
Under the hood, Matching Engine leverages ScaNN, which uses techniques like anisotropic vector quantization and tree-based partitioning to prune search space. In production, a common pitfall is that the index must be rebuilt when embeddings change (e.g., after model retraining), which can take hours for billion-scale datasets; real-time updates are only supported via a separate streaming index with limited consistency guarantees. This makes it critical to plan for offline indexing cycles in MLOps pipelines.
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
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.
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 PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Lower query latency for large datasets — Vertex AI Matching Engine uses approximate nearest neighbor (ANN) algorithms like ScaNN (Scalable Nearest Neighbors) to index high-dimensional vectors. For large datasets, ANN dramatically reduces query latency by avoiding a full scan of all vectors, unlike brute-force search which must compute distances against every vector. This makes option C correct because ANN trades a negligible accuracy loss for orders-of-magnitude faster retrieval.
What should I do if I get this PMLE 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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