- A
Use Vertex AI Endpoint with a larger machine type and gRPC
Why wrong: gRPC is supported but request size limit still applies.
- B
Use Vertex AI Batch Prediction
Why wrong: Batch prediction is for offline inference, not online.
- C
Split the model into smaller parts and deploy multiple endpoints
Why wrong: Impractical and adds latency.
- D
Deploy the model on a Compute Engine VM with a custom container and a load balancer
Bypasses Vertex AI Prediction limits; you can handle large payloads.
Question 415 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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.
Your team is deploying a large language model (LLM) on Vertex AI for online prediction. The model exceeds the maximum request size for Vertex AI Prediction. Which approach should you take to serve this model?
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 the model on a Compute Engine VM with a custom container and a load balancer
Vertex AI Prediction has a request size limit (1.5 MB). Using a custom container with a ModelServer (e.g., TensorFlow Serving) behind an HTTP load balancer bypasses this limit and allows large payloads.
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.
- ✗
Use Vertex AI Endpoint with a larger machine type and gRPC
Why it's wrong here
gRPC is supported but request size limit still applies.
- ✗
Use Vertex AI Batch Prediction
Why it's wrong here
Batch prediction is for offline inference, not online.
- ✗
Split the model into smaller parts and deploy multiple endpoints
Why it's wrong here
Impractical and adds latency.
- ✓
Deploy the model on a Compute Engine VM with a custom container and a load balancer
Why this is correct
Bypasses Vertex AI Prediction limits; you can handle large payloads.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Scaling Prototypes into ML Models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the model on a Compute Engine VM with a custom container and a load balancer — Vertex AI Prediction has a request size limit (1.5 MB). Using a custom container with a ModelServer (e.g., TensorFlow Serving) behind an HTTP load balancer bypasses this limit and allows large payloads.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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