Google Cloud Professional Machine Learning Engineer — Question 207

You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

Answer options

• A. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the BigQueryJobOp component to run the preprocessing script and the CustomTrainingJobOp component to launch a Vertex AI training job.
• B. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the DataflowPythonJobOp component to preprocess the data and the CustomTrainingJobOp component to launch a Vertex AI training job.
• C. Use the TensorFlow Extended SDK to implement the pipeline Use the ExampleGen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex AI training job.
• D. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model. Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex AI training job.

Correct answer: A

Explanation

The correct answer is A because using the Kubeflow Pipelines SDK with the BigQueryJobOp allows for efficient execution of the preprocessing script directly in BigQuery, streamlining the workflow. Options B and C suggest alternatives that either do not utilize BigQuery effectively or use different SDKs that may not be as suited for this scenario. Option D complicates the process by embedding preprocessing in the input function, which is less efficient for a training pipeline.