hardMultiple ChoiceObjective-mapped
PDE Practice Question: Responsible for deploying a PyTorch model for…
You are responsible for deploying a PyTorch model for real-time inference. The model requires GPU acceleration. You want to minimize infrastructure management overhead. Which serving option should you choose?
⚠ Common exam trap
Google Cloud often tests the misconception that Cloud Run or Cloud Functions can support GPUs, but in reality, neither service offers GPU acceleration, making Vertex AI Endpoints the only viable managed option for GPU inference.
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
✓
Deploy the model on Vertex AI Endpoints using a custom container with GPU support
Vertex AI Endpoints with a custom container and GPU support is the correct choice because it is purpose-built for serving ML models at scale, fully managed, and supports GPU acceleration for low-latency inference. It minimizes infrastructure overhead by handling auto-scaling, health checks, and model versioning, unlike the other options that lack GPU support or are designed for training rather than serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model as a Cloud Function with a GPU backend
Why it's wrong here
Cloud Functions do not support GPU.
- ✗
Use Cloud Run with GPU enabled
Why it's wrong here
Cloud Run does not currently support GPUs.
- ✗
Use AI Platform Training to host the model as a prediction service
Why it's wrong here
AI Platform Training is for training jobs, not serving.
- ✓
Deploy the model on Vertex AI Endpoints using a custom container with GPU support
Why this is correct
Vertex AI supports custom containers and GPUs for serving.
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This PDE 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 PDE exam.