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…

Question

Options

• ARun the TFX pipeline in Vertex AI Pipelines. Configure the pipeline to use Vertex AI Training jobs
• BRun the TFX pipeline in Vertex AI Pipelines. Set the appropriate Apache Beam parameters in the
• CRun the TFX pipeline in Dataproc by using the Apache Beam TFX orchestrator. Set the
• DRun the TFX pipeline in Dataflow by using the Apache Beam TFX orchestrator. Set the appropriate

• A
  15% (5)
• B
  76% (26)
• C
  6% (2)
• D
  3% (1)

Explanation

This configuration allows you to leverage the scalability of Dataflow for processing large datasets (up to 100 TB) efficiently while running the TFX pipeline within Vertex AI Pipelines. By using Apache Beam parameters, you can optimize the data preprocessing steps to handle large volumes of data effectively. Additionally, integrating with Vertex AI Pipelines ensures that you can publish metrics and parameters to Vertex AI Experiments and track artifacts using Vertex ML Metadata, fulfilling all your requirements for scalability, monitoring, and metadata management.

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