PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #58
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #58: Real Exam Question with Answer & Explanation
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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
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