- A
Batch rebuild the index every hour
Why wrong: Batch rebuilds are time-consuming and cause downtime; not suitable for hourly updates.
- B
Use streaming updates to add new embeddings incrementally
Correct: Streaming updates allow near-real-time ingestion of new vectors.
- C
Create a new index each hour and swap endpoints
Why wrong: Swapping endpoints is complex and may cause query interruption.
- D
Use brute-force index to simplify updates
Why wrong: Brute-force index does not support streaming updates; also inefficient for large datasets.
Question 577 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 (Matching Engine) for a product recommendation system. The product embeddings are updated hourly. Which index update method should they use to ensure low latency for new items?
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
✓
Use streaming updates to add new embeddings incrementally
Option B is correct because Vertex AI Vector Search supports streaming updates, allowing new embeddings to be added incrementally without rebuilding the entire index. This ensures low latency for new items by making them searchable almost immediately after update, which is critical for hourly refresh cycles where batch rebuilds would introduce significant delay.
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.
- ✗
Batch rebuild the index every hour
Why it's wrong here
Batch rebuilds are time-consuming and cause downtime; not suitable for hourly updates.
- ✓
Use streaming updates to add new embeddings incrementally
Why this is correct
Correct: Streaming updates allow near-real-time ingestion of new vectors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a new index each hour and swap endpoints
Why it's wrong here
Swapping endpoints is complex and may cause query interruption.
- ✗
Use brute-force index to simplify updates
Why it's wrong here
Brute-force index does not support streaming updates; also inefficient for large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume batch rebuilds are the only reliable method for consistency, overlooking that streaming updates in Vertex AI Vector Search are designed specifically for low-latency incremental ingestion without sacrificing search quality.
Detailed technical explanation
How to think about this question
Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) under the hood, which supports both batch and streaming update modes. Streaming updates leverage a delta index structure that merges new embeddings into the existing ANN index incrementally, maintaining low query latency by avoiding full re-indexing. In real-world scenarios, this is crucial for e-commerce platforms where new products must appear in recommendations within minutes of catalog updates, not after a full rebuild cycle.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Use streaming updates to add new embeddings incrementally — Option B is correct because Vertex AI Vector Search supports streaming updates, allowing new embeddings to be added incrementally without rebuilding the entire index. This ensures low latency for new items by making them searchable almost immediately after update, which is critical for hourly refresh cycles where batch rebuilds would introduce significant delay.
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
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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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