- A
Switch to a streaming update configuration for the index.
Streaming updates allow near real-time index updates, making new products searchable within minutes.
- B
Deploy multiple index endpoints and use traffic splitting.
Why wrong: This does not address the update latency issue.
- C
Increase the frequency of the batch update job to run every 30 minutes.
Why wrong: Batch updates still have latency and may not achieve sub-hour visibility due to processing time.
- D
Use a brute-force index instead of an ANN index.
Why wrong: Brute-force index does not support streaming updates and is slower for large datasets.
Question 823 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 (Vector Search) to serve similarity search for an e-commerce product recommendation system. The index is updated daily with new product embeddings via a batch job. However, you notice that some new products are not appearing in the search results for up to 24 hours. You need to ensure that new products are discoverable within 1 hour of ingestion. What should you do?
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
✓
Switch to a streaming update configuration for the index.
Option A is correct because Vertex AI Matching Engine supports streaming updates to the index, which allows new embeddings to be added in near real-time (typically within minutes) without requiring a full batch rebuild. By switching to a streaming update configuration, new product embeddings become searchable within the 1-hour requirement, as the index is updated incrementally as data arrives.
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.
- ✓
Switch to a streaming update configuration for the index.
Why this is correct
Streaming updates allow near real-time index updates, making new products searchable within minutes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy multiple index endpoints and use traffic splitting.
Why it's wrong here
This does not address the update latency issue.
- ✗
Increase the frequency of the batch update job to run every 30 minutes.
Why it's wrong here
Batch updates still have latency and may not achieve sub-hour visibility due to processing time.
- ✗
Use a brute-force index instead of an ANN index.
Why it's wrong here
Brute-force index does not support streaming updates and is slower for large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume increasing batch frequency (Option C) is sufficient, but they overlook that batch updates in Vertex AI Matching Engine are designed for daily or less frequent rebuilds and cannot guarantee sub-hour freshness due to build and deployment overhead, whereas streaming updates are the intended solution for low-latency index updates.
Detailed technical explanation
How to think about this question
Vertex AI Matching Engine's streaming updates work by sending new embeddings to a dedicated streaming endpoint that writes to a delta index, which is merged with the base index periodically (e.g., every few minutes) without downtime. This is distinct from batch updates, which require a full index rebuild and redeployment, often taking hours. In production, streaming is critical for use cases like real-time product catalog updates or user-generated content where latency to searchability must be under an hour.
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: Switch to a streaming update configuration for the index. — Option A is correct because Vertex AI Matching Engine supports streaming updates to the index, which allows new embeddings to be added in near real-time (typically within minutes) without requiring a full batch rebuild. By switching to a streaming update configuration, new product embeddings become searchable within the 1-hour requirement, as the index is updated incrementally as data arrives.
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.
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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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