The question involves optimizing a TensorFlow Extended (TFX) pipeline to efficiently process 100TB of data from BigQuery while leveraging Vertex AI for metrics, parameters, and artifact tracking.
The correct answer is B. This approach leverages Vertex AI Pipelines for orchestration and Apache Beam's Dataflow capabilities for scalable data preprocessing.
You are developing a TensorFlow Extended (TFX) pipeline with standard TFX components. The pipeline includes data preprocessing steps. After the pipeline is deployed to production, it will process up to 100 TB of data stored in BigQuery. You need the data preprocessing steps to scale efficiently, publish metrics and parameters to Vertex AI Experiments, and track artifacts by using Vertex ML Metadata. How should you configure the pipeline run?
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