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.”
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 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?
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 dynamically adjusts capacity to handle peak load, and ensuring sufficient GPU quota prevents resource constraints. Option B is wrong because statically increasing replicas leads to resource waste during low traffic and may not react quickly to spikes. Option C is wrong because increasing CPU resources does not address GPU-bound inference. Option D is wrong because Cloud Functions is not designed for GPU-accelerated inference and introduces additional latency.
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.
Read the scenario before looking for a memorised answer.
✗
Switch to a larger machine type with more vCPUs.
Why it's wrong here
The model is GPU-bound; increasing CPU does not address the bottleneck.
✗
Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.
Why it's wrong here
Static scaling wastes resources during low traffic and may not react quickly to sudden spikes.
✗
Use Cloud Functions to invoke the model asynchronously.
Why it's wrong here
Cloud Functions lacks GPU support and adds latency; 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
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 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.
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: 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 dynamically adjusts capacity to handle peak load, and ensuring sufficient GPU quota prevents resource constraints. Option B is wrong because statically increasing replicas leads to resource waste during low traffic and may not react quickly to spikes. Option C is wrong because increasing CPU resources does not address GPU-bound inference. Option D is wrong because Cloud Functions is not designed for GPU-accelerated inference and introduces additional latency.
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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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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Question Discussion
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