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PMLE Practice Question: A data science team is deploying a PyTorch model…
A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between deployment-time compatibility errors and runtime health check failures, tricking candidates into confusing a misconfigured machine type with a failing health probe.
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
✓
The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.
The error 'The container image is not compatible with the machine type' indicates a mismatch between the container's hardware requirements and the machine type selected for the Vertex AI Endpoint. Since the custom container requires specific CUDA drivers, it is built for GPU acceleration. If the endpoint is configured with a CPU-only machine type (e.g., n1-standard-4), the container will fail to run because the GPU drivers cannot initialize, triggering this incompatibility error.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The service account does not have permission to pull the container from Artifact Registry.
Why it's wrong here
Permission issues cause unauthorized errors, not compatibility errors.
- ✓
The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.
Why this is correct
CUDA drivers require GPU machines; using a CPU machine causes compatibility error.
- ✗
The container's health check endpoint is not responding correctly.
Why it's wrong here
Health check failures produce different errors.
- ✗
The model artifact size exceeds the maximum allowed for the machine type.
Why it's wrong here
Size issues cause timeout or memory errors, not compatibility errors.
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