- A
Brute-force index with cosine distance.
Why wrong: Brute-force is exact but extremely slow for 10M vectors; cannot meet 10ms latency.
- B
Approximate Nearest Neighbor (ANN) index using the ScaNN algorithm.
ANN with ScaNN is designed for low-latency, high-scale similarity search.
- C
A custom distance-based index using Cloud SQL.
Why wrong: Cloud SQL cannot efficiently handle vector search at this scale.
- D
A tree-based index from scikit-learn deployed as a custom container.
Why wrong: Tree-based indices may not scale to 10M points and are not optimized for low latency.
Question 893 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.
You are using Vertex AI Matching Engine for similarity search. Your index has 10 million embeddings of 512 dimensions. The query latency requirement is under 10ms for 99th percentile. Which index type should you choose?
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 using the ScaNN algorithm.
Option B is correct because the ScaNN (Scalable Nearest Neighbors) algorithm is specifically designed for high-dimensional, large-scale similarity search with strict latency requirements. With 10 million 512-dimensional embeddings, an ANN index like ScaNN can achieve sub-10ms query latency at the 99th percentile by trading a small amount of recall for dramatic speed improvements, which is exactly what Vertex AI Matching Engine optimizes for.
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.
- ✗
Brute-force index with cosine distance.
Why it's wrong here
Brute-force is exact but extremely slow for 10M vectors; cannot meet 10ms latency.
- ✓
Approximate Nearest Neighbor (ANN) index using the ScaNN algorithm.
Why this is correct
ANN with ScaNN is designed for low-latency, high-scale similarity search.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A custom distance-based index using Cloud SQL.
Why it's wrong here
Cloud SQL cannot efficiently handle vector search at this scale.
- ✗
A tree-based index from scikit-learn deployed as a custom container.
Why it's wrong here
Tree-based indices may not scale to 10M points and are not optimized for low latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume brute-force is the only 'accurate' option and underestimate how severely the curse of dimensionality degrades tree-based and exact methods at 512 dimensions, leading them to pick A or D despite the explicit latency constraint.
Detailed technical explanation
How to think about this question
ScaNN uses anisotropic quantization and score-aware loss to compress vectors while preserving relative distances, enabling fast approximate inner product search via asymmetric hashing. In practice, the trade-off between recall and latency is controlled by the number of probes and the quantization granularity; for 10M vectors, ScaNN can achieve >95% recall at 5ms latency by partitioning the space with a tree structure and then scoring only the most promising leaf nodes. A real-world scenario is a recommendation system where a 1% drop in recall is acceptable to serve 1000 queries per second under 10ms, making ANN the only viable choice.
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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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Approximate Nearest Neighbor (ANN) index using the ScaNN algorithm. — Option B is correct because the ScaNN (Scalable Nearest Neighbors) algorithm is specifically designed for high-dimensional, large-scale similarity search with strict latency requirements. With 10 million 512-dimensional embeddings, an ANN index like ScaNN can achieve sub-10ms query latency at the 99th percentile by trading a small amount of recall for dramatic speed improvements, which is exactly what Vertex AI Matching Engine optimizes for.
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 →
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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