easyMultiple ChoiceObjective-mapped
PDE Practice Question: A company trains a custom model using TensorFlow…
A company trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?
⚠ Common exam trap
Google Cloud often tests the misconception that Cloud Run or Cloud Functions can handle large models for real-time inference, ignoring their size limits, cold-start latency, and lack of native Vertex AI integration for model management and scaling.
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 to Vertex AI Endpoint with a custom container
Deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by keeping the model loaded in memory across requests, and it can scale to handle real-time inference traffic, unlike batch or serverless options that have cold-start or size limitations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Vertex AI Batch Prediction job
Why it's wrong here
Batch is not for real-time.
- ✗
Deploy as a Cloud Function
Why it's wrong here
Cloud Functions have memory limits.
- ✓
Deploy to Vertex AI Endpoint with a custom container
Why this is correct
Custom containers allow large models.
- ✗
Deploy to Cloud Run with minimum instances
Why it's wrong here
Cloud Run also has memory limits.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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