- A
Deploy multiple model versions on the same endpoint with traffic_split parameter.
Vertex AI supports this natively for A/B testing.
- B
Deploy each model as a separate endpoint and use Cloud Load Balancing.
Why wrong: Separate endpoints complicate traffic splitting and increase cost.
- C
Enable Vertex AI Model Monitoring to detect prediction drift.
Model Monitoring helps ensure quality of served models.
- D
Use Cloud Monitoring to create custom metrics based on business outcomes.
Custom metrics allow measurement of business impact (e.g., conversion rate).
- E
Export logs to BigQuery for manual analysis only.
Why wrong: Manual analysis is insufficient; automated monitoring is needed.
Question 302 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 is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)
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
✓
Deploy multiple model versions on the same endpoint with traffic_split parameter.
Option A is correct because Vertex AI endpoints support deploying multiple model versions and using the `traffic_split` parameter to distribute traffic percentages among them. This allows the company to serve different models from a single endpoint while controlling the proportion of requests each model receives, meeting the requirement for a unified serving infrastructure.
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.
- ✓
Deploy multiple model versions on the same endpoint with traffic_split parameter.
Why this is correct
Vertex AI supports this natively for A/B testing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy each model as a separate endpoint and use Cloud Load Balancing.
Why it's wrong here
Separate endpoints complicate traffic splitting and increase cost.
- ✓
Enable Vertex AI Model Monitoring to detect prediction drift.
Why this is correct
Model Monitoring helps ensure quality of served models.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Cloud Monitoring to create custom metrics based on business outcomes.
Why this is correct
Custom metrics allow measurement of business impact (e.g., conversion rate).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Export logs to BigQuery for manual analysis only.
Why it's wrong here
Manual analysis is insufficient; automated monitoring is needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think separate endpoints with a load balancer (Option B) are required for traffic distribution, overlooking Google Vertex AI's built-in traffic splitting on a single endpoint, which is simpler and more aligned with the platform's design.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring (Option C) automatically computes distribution statistics for prediction inputs and outputs, comparing them against a baseline to detect drift using techniques like the Jensen-Shannon divergence or the L-infinity distance. Cloud Monitoring (Option D) can ingest custom metrics derived from business outcomes (e.g., conversion rates) and trigger alerts when prediction quality degrades, enabling a closed-loop feedback system. The `traffic_split` parameter in Vertex AI uses a map of model version IDs to integer percentages that must sum to 100, and it supports gradual rollouts and A/B testing.
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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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: Deploy multiple model versions on the same endpoint with traffic_split parameter. — Option A is correct because Vertex AI endpoints support deploying multiple model versions and using the `traffic_split` parameter to distribute traffic percentages among them. This allows the company to serve different models from a single endpoint while controlling the proportion of requests each model receives, meeting the requirement for a unified serving infrastructure.
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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