PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #197
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #197: Real Exam Question with Answer & Explanation
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Question
You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex AI custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?
Options
- ACreate a Vertex AI Workbench notebook. Use the notebook to submit the Dataproc Serverless
- BCreate a Vertex AI Workbench notebook. Initiate an Apache Spark context in the notebook and
- CUse the Kubeflow pipelines SDK to write code that specifies two components:
- DUse the Kubeflow pipelines SDK to write code that specifies two components
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