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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #58

You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new…

The correct answer is A. Deploy model in test environment -> Evaluate and test model -> Create a new AI Platform model. A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production. B is not correct because the model cannot be validated before being deployed to the test C is not…

Submitted by klara.se· Apr 18, 2026ML pipeline operationalization

Question

You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

Options

  • ADeploy model in test environment -> Evaluate and test model -> Create a new AI Platform model
  • BValidate model -> Deploy model in test environment -> Create a new AI Platform model version
  • CCreate a new AI Platform model version -> Evaluate and test model -> Deploy model in test
  • DCreate a new AI Platform model version - > Deploy model in test environment -> Validate model

How the community answered

(34 responses)
  • A
    79% (27)
  • B
    3% (1)
  • C
    12% (4)
  • D
    6% (2)

Explanation

A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production. B is not correct because the model cannot be validated before being deployed to the test C is not correct because the model version is being set up for the release candidate before the model is validated. Moreover, the model cannot be validated before being deployed to the test D is not correct because the model version is being set up for the release candidate before the model is validated. https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow- pipelines-and-cloud-build

Topics

#Model deployment#Kubeflow pipelines#AI Platform#Model testing

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