- A
Brute-force (flat) index
Why wrong: Brute-force is accurate but slower for large datasets.
- B
Approximate nearest neighbor (ANN) index with Scann
ANN is designed for large-scale, low-latency search with high recall.
- C
Tree-based index
Why wrong: Tree-based not supported in Vertex AI Vector Search.
- D
Hashing-based index
Why wrong: Not a standard index type in Vector Search.
Question 463 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 uses Vertex AI Vector Search for similarity search. They have a dataset of 10 million 512-dimensional vectors. Which index type should they choose for lowest latency at high recall?
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 a dataset of 10 million 512-dimensional vectors, a brute-force (flat) index would be far too slow for low-latency queries. Approximate Nearest Neighbor (ANN) with ScaNN (Scalable Nearest Neighbors) is specifically designed by Google for high-dimensional vector search, offering sub-linear query time while maintaining high recall through techniques like anisotropic quantization and tree-based partitioning. This makes it the optimal choice for balancing latency and recall at this scale.
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 (flat) index
Why it's wrong here
Brute-force is accurate but slower for large datasets.
- ✓
Approximate nearest neighbor (ANN) index with Scann
Why this is correct
ANN is designed for large-scale, low-latency search with high recall.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Tree-based index
Why it's wrong here
Tree-based not supported in Vertex AI Vector Search.
- ✗
Hashing-based index
Why it's wrong here
Not a standard index type in Vector Search.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume brute-force is the only way to guarantee high recall, but the question explicitly asks for lowest latency at high recall, which is the exact trade-off that ANN indexes like ScaNN are designed to optimize.
Detailed technical explanation
How to think about this question
ScaNN uses a two-stage approach: first, it partitions the vector space using a tree structure (e.g., a k-means tree) to prune the search space, then applies anisotropic quantization to score only the top candidates, which reduces the number of distance computations. In practice, ScaNN can achieve 95% recall with query latency under 10ms on a single machine for millions of 512-dimensional vectors, whereas brute-force would take hundreds of milliseconds. A subtle behavior is that ScaNN's performance is highly sensitive to the number of leaves to search and the quantization bitwidth, requiring careful tuning for the specific dataset.
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: Approximate nearest neighbor (ANN) index with Scann — For a dataset of 10 million 512-dimensional vectors, a brute-force (flat) index would be far too slow for low-latency queries. Approximate Nearest Neighbor (ANN) with ScaNN (Scalable Nearest Neighbors) is specifically designed by Google for high-dimensional vector search, offering sub-linear query time while maintaining high recall through techniques like anisotropic quantization and tree-based partitioning. This makes it the optimal choice for balancing latency and recall at this scale.
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
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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