Model Deployment — Google Cloud ML Engineer Practice Questions
Model deployment on Google Cloud involves packaging a trained model and serving it for online predictions, batch predictions, or edge inference using Vertex AI Endpoints or other serving infrastructure. The Google Cloud ML Engineer exam covers deploying models to Vertex AI, configuring machine types for serving, setting traffic splits, and managing endpoint scaling. You are also expected to understand containerized serving using custom containers and when to use batch versus online prediction. Deployment decisions directly affect cost, latency, and availability of the ML system in production.
Free questions on model deployment
Which approach should be used when deploying machine learning models to production?
Free question · medium · full answer + explanation
Which Google Cloud service is specifically designed for building and deploying machine learning models?
Free question · easy · full answer + explanation
Which Google Cloud service provides a unified platform for building, deploying, and managing ML models?
Free question · easy · full answer + explanation
More model deployment questions in the full bank
- What is the benefit of shadow deployments? Unlock answer & explanation →
- Which GCP service allows you to serve trained models with automatic scaling? Unlock answer & explanation →
- In Vertex AI, what is the primary purpose of deploying a model to a custom prediction endpoint rather than using batch prediction? Unlock answer & explanation →