easyMultiple Choice
PDE Practice Question: A data scientist has trained an XGBoost model on…
A data scientist has trained an XGBoost model on Vertex AI and wants to deploy it to an endpoint with automatic scaling based on traffic. What is the recommended deployment approach?
⚠ Common exam trap
Google Cloud often tests the distinction between online (real-time) and batch prediction services, and the trap here is that candidates may confuse Vertex AI Endpoints with generic serverless options like Cloud Run or App Engine, overlooking the fact that Vertex AI provides a purpose-built, managed endpoint service with native autoscaling for ML models.
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 Vertex AI Endpoints with automatic scaling enabled
Vertex AI Endpoints with automatic scaling enabled is the recommended approach because it directly supports deploying trained models (including XGBoost) as online prediction endpoints with built-in autoscaling based on incoming traffic. This service manages the underlying infrastructure, load balancing, and scaling policies, aligning with the requirement for automatic scaling without additional containerization or serverless overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the model to a container and deploy on Cloud Run
Why it's wrong here
Cloud Run scales HTTP containers, but it provides no Vertex AI model registry, prediction runtime, or endpoint monitoring; the container must implement its own inference server. It tempts teams wanting serverless simplicity, yet Vertex AI endpoints already deliver managed autoscaling for XGBoost.
- ✗
Use AI Platform Prediction with batch prediction
Why it's wrong here
Batch prediction processes stored data in scheduled jobs and returns results to Cloud Storage; it creates no endpoint and performs no traffic-driven autoscaling. It is tempting because it suits large offline scoring runs, but the stem requires a live, scaling endpoint serving online requests.
- ✗
Deploy the model as an API on App Engine
Why it's wrong here
App Engine hosts web applications and scales app instances, not ML prediction servers; it lacks Vertex AI's model serving runtime, explainability, and endpoint traffic splitting. It appeals as a familiar managed platform, but the stem requires a Vertex AI endpoint with automatic traffic-based scaling.
- ✓
Use Vertex AI Endpoints with automatic scaling enabled
Why this is correct
Vertex AI Endpoints natively support automatic scaling, adjusting replica counts to match traffic without manual intervention. Deploying the trained XGBoost model there satisfies the stem's traffic-based scaling requirement, unlike batch prediction or a fixed single-replica deployment.
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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