- A
Tree-based index
Why wrong: Not a supported index type in Vertex AI Vector Search.
- B
Approximate nearest neighbor (ANN) index using ScaNN
ScaNN is designed for efficient large-scale similarity search with configurable accuracy.
- C
Brute-force index
Why wrong: Brute-force would be too slow for 10M vectors.
- D
Hash-based index
Why wrong: Not a supported index type.
Question 473 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 need to serve a large embedding model for similarity search with low latency. The model was trained to generate 256-dimensional embeddings. You plan to use Vertex AI Vector Search. Which index type should you choose to balance accuracy and performance for a dataset with 10 million vectors?
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 ScaNN
Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) as its underlying ANN algorithm, which is specifically designed for high-dimensional embeddings (like 256-d) and large-scale datasets (10M vectors). ScaNN balances accuracy and performance by employing anisotropic quantization and tree-based partitioning, making it the optimal choice for low-latency similarity search without requiring exhaustive comparison.
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.
- ✗
Tree-based index
Why it's wrong here
Not a supported index type in Vertex AI Vector Search.
- ✓
Approximate nearest neighbor (ANN) index using ScaNN
Why this is correct
ScaNN is designed for efficient large-scale similarity search with configurable accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Brute-force index
Why it's wrong here
Brute-force would be too slow for 10M vectors.
- ✗
Hash-based index
Why it's wrong here
Not a supported index type.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly choose Tree-based index (Option A) because ScaNN uses tree-based partitioning internally, but a standalone tree index fails in high dimensions. Vertex AI Vector Search’s ScaNN combines tree partitioning with quantization to overcome the curse of dimensionality and balance accuracy and performance for 10 million 256-d vectors.
Detailed technical explanation
How to think about this question
ScaNN (Scalable Nearest Neighbors) uses a two-stage approach: first, it partitions the vector space using a tree structure (e.g., VP-tree or spill tree) to prune the search space, then it applies anisotropic quantization to compress the remaining candidates, enabling fast distance computations via SIMD-optimized dot products. In practice, for 256-dimensional embeddings, ScaNN can achieve recall rates above 95% while reducing latency by orders of magnitude compared to brute-force, making it the default choice for production-scale similarity search on Vertex AI.
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 ScaNN — Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) as its underlying ANN algorithm, which is specifically designed for high-dimensional embeddings (like 256-d) and large-scale datasets (10M vectors). ScaNN balances accuracy and performance by employing anisotropic quantization and tree-based partitioning, making it the optimal choice for low-latency similarity search without requiring exhaustive comparison.
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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