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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #225

You are developing an ML pipeline using Vertex AI Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex AI Model Registry and deploy it to Vertex AI Endpoints for on

The correct answer is D. Chain the Vertex AI ModelUploadOp and ModelDeployOp components together. The simplest way to upload and deploy an XGBoost model within a Vertex AI Pipeline is to directly use and chain the built-in Vertex AI ModelUploadOp and ModelDeployOp components.

Submitted by tarun92· Apr 18, 2026ML pipeline operationalization

Question

You are developing an ML pipeline using Vertex AI Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex AI Model Registry and deploy it to Vertex AI Endpoints for online inference. You want to use the simplest approach. What should you do?

Options

  • AUse the Vertex AI REST API within a custom component based on a vertex-ai/prediction/xgboost-
  • BUse the Vertex AI ModelEvaluationOp component to evaluate the model
  • CUse the Vertex AI SDK for Python within a custom component based on a python:3.10 image
  • DChain the Vertex AI ModelUploadOp and ModelDeployOp components together

How the community answered

(46 responses)
  • A
    11% (5)
  • B
    4% (2)
  • C
    4% (2)
  • D
    80% (37)

Why each option

The simplest way to upload and deploy an XGBoost model within a Vertex AI Pipeline is to directly use and chain the built-in `Vertex AI ModelUploadOp` and `ModelDeployOp` components.

AUse the Vertex AI REST API within a custom component based on a vertex-ai/prediction/xgboost-

Using the Vertex AI REST API within a custom component is technically possible but far more complex than using the pre-built pipeline components.

BUse the Vertex AI ModelEvaluationOp component to evaluate the model

`Vertex AI ModelEvaluationOp` is used for evaluating models, not for uploading or deploying them.

CUse the Vertex AI SDK for Python within a custom component based on a python:3.10 image

Using the Vertex AI SDK for Python within a custom component is also technically possible but more complex than using the pre-built components, requiring managing custom code and environments.

DChain the Vertex AI ModelUploadOp and ModelDeployOp components togetherCorrect

Vertex AI Pipelines offers pre-built components for common ML operations, including `ModelUploadOp` for registering models to the Model Registry and `ModelDeployOp` for deploying them to an Endpoint. Chaining these two components together is the most direct and simplest way to achieve the desired outcome.

Concept tested: Vertex AI Pipelines built-in components for model management

Source: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#upload-model-and-deploy-to-endpoint

Topics

#Vertex AI Pipelines#Model Deployment#Model Registry#Pre-built Components

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