- A
Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.
Option A is correct because enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion.
- B
Switch to a larger machine type with more vCPUs.
Why wrong: Option B is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes.
- C
Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.
Why wrong: Option C is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU.
- D
Use Cloud Functions to invoke the model asynchronously.
Why wrong: Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.
Autoscaling GPU Models on Vertex AI Prediction — Ensuring GPU Quota
This PMLE practice question tests your understanding of pmle exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?
Quick Answer
The correct answer is to enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, while ensuring GPU quota is sufficient. This approach dynamically adjusts compute resources in response to traffic, preventing latency spikes and timeouts during surges without wasting resources during idle periods. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI Prediction’s autoscaling behavior and the critical role of GPU quota—a common trap is assuming static scaling or CPU upgrades solve GPU-bound bottlenecks. Remember, autoscaling relies on quota headroom; without pre-allocated GPU quota, the service cannot spin up additional replicas under load. A simple memory tip: “Min for base, max for burst, quota for worst.”
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
Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.
Option A is correct because enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion. Option B is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU. Option C is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes. Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.
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.
- ✓
Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.
Why this is correct
Option A is correct because enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a larger machine type with more vCPUs.
Why it's wrong here
Option B is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes.
- ✗
Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.
Why it's wrong here
Option C is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU.
- ✗
Use Cloud Functions to invoke the model asynchronously.
Why it's wrong here
Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient. — Option A is correct because enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion. Option B is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU. Option C is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes. Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.
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
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 →
Same concept, more angles
1 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a model for online predictions on Vertex AI. They want to minimize latency while also handling traffic spikes. Which TWO configurations should they choose?
medium- ✓ A.Use GPU machine type
- ✓ B.Enable autoscaling with min replicas=1
- C.Disable autoscaling and use manual scaling
- D.Use CPU machine type with more memory
- E.Set a fixed number of replicas equal to peak load
Why A: Option A is correct because GPU machine types on Vertex AI provide significantly faster inference for deep learning models, reducing latency per prediction. Option B is correct because enabling autoscaling with min replicas=1 ensures the model can handle traffic spikes by dynamically adding replicas while keeping at least one instance running to avoid cold starts.
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Last reviewed: Jun 24, 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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