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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #298
You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evalua
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Submitted by kwame.gh· Apr 18, 2026ML pipeline operationalization
Question
You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?
Options
- ASet up a TensorFlow Extended (TFX) pipeline on Vertex AI Pipelines to orchestrate the MLOps
- BSet up a Vertex AI Pipelines to orchestrate the MLOps pipeline. Use the predefined Dataproc
- CSet up Kubeflow Pipelines on Google Kubernetes Engine to orchestrate the MLOps pipeline. Write
- DSet up Cloud Composer to orchestrate the MLOps pipeline. Use Dataproc workflow templates for
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Topics
#MLOps pipelines#Vertex AI Pipelines#Dataproc#Pipeline orchestration