This PMLE practice question tests your understanding of serving and scaling models. 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 data science team deploys a PyTorch model using Vertex AI Prediction. The model requires GPU for inference, but they notice high costs and underutilized GPUs during off-peak hours. What is the most cost-effective solution?
Move the model to Cloud Functions
Why wrong: Cloud Functions are not designed for GPU model serving.
Use a GPU instance with a fixed number of replicas
Why wrong: Fixed replicas lead to underutilization during off-peak.
Use a GPU instance with min replicas=0 and autoscaling
Scales down to zero when unused, saving costs.
Switch to a CPU-only machine type
Why wrong: Model requires GPU, CPU-only will not work.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a GPU instance with min replicas=0 and autoscaling
Option C is correct because setting min replicas to 0 allows Vertex AI Prediction to scale down to zero instances during off-peak hours, eliminating GPU costs when no requests are being served. Combined with autoscaling, the deployment will spin up GPU-backed instances on demand only when traffic arrives, directly addressing the underutilization issue while maintaining low latency for inference requests.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Move the model to Cloud Functions
Why it's wrong here
Cloud Functions are not designed for GPU model serving.
Use a GPU instance with a fixed number of replicas
Why it's wrong here
Fixed replicas lead to underutilization during off-peak.
Use a GPU instance with min replicas=0 and autoscaling
Why this is correct
Scales down to zero when unused, saving costs.
Related concept
Read the scenario before looking for a memorised answer.
Switch to a CPU-only machine type
Why it's wrong here
Model requires GPU, CPU-only will not work.
Common exam traps
Google Cloud often tests the misconception that autoscaling alone reduces costs, but the trap here is that without setting min replicas to 0, you still pay for idle GPU instances during off-peak hours, which is the exact problem described in the question.
Detailed technical explanation
Vertex AI Prediction uses the `minReplicaCount` parameter in the `DeployedModel` resource to control the minimum number of serving containers. Setting it to 0 leverages the platform's ability to scale to zero, which is distinct from typical Kubernetes HPA behavior where min replicas are usually >=1. Under the hood, Vertex AI uses a custom autoscaler that monitors request queue depth and CPU/GPU utilization, and when minReplicaCount=0, it can completely tear down the underlying Compute Engine instances, stopping GPU billing entirely. A real-world scenario is a batch inference pipeline that runs nightly—setting min replicas to 0 ensures no GPU cost during the day when no jobs are scheduled.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: Use a GPU instance with min replicas=0 and autoscaling — Option C is correct because setting min replicas to 0 allows Vertex AI Prediction to scale down to zero instances during off-peak hours, eliminating GPU costs when no requests are being served. Combined with autoscaling, the deployment will spin up GPU-backed instances on demand only when traffic arrives, directly addressing the underutilization issue while maintaining low latency for inference requests.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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