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PMLE Practice Question: A data science team deploys a PyTorch model using…
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?
⚠ Common exam trap
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.
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
✓
Use a GPU instance with min replicas=0 and autoscaling
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
Switch to a CPU-only machine type
Why it's wrong here
Model requires GPU, CPU-only will not work.
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