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Answer: Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
The correct answer is D. Using Apache Beam with TensorFlow Transform (tf.Transform) and Dataflow is recommended when you need to apply consistent instance-level data transformations during both model training and serving. This approach ensures that the same preprocessing logic is applied in both scenarios, which is essential for maintaining consistency and accuracy in your model predictions. Vertex AI and custom preprocessing components or functions can complicate the process and may not guarantee the same level of consistency, especially for real-time inference scenarios.
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You are developing a custom TensorFlow model intended for online predictions. The training data resides in BigQuery, and you need to apply instance-level data transformations to this data for both model training and serving. Consistency is key, so you want to utilize the same preprocessing routine during both phases. How should you configure the preprocessing routine to achieve this consistency?
A
Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
B
Create a pipeline in Vertex AI Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
C
Create a preprocessing function that reads and transforms the data from BigQuery. Create a Vertex AI custom prediction routine that calls the preprocessing function at serving time.
D
Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.