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Answer: Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataFlowPythonJobOp and the ModelBatchPredictOp components.
The most suitable option is C. You do not need to deploy the model to an endpoint since you are looking for batch predictions rather than real-time predictions. The ModelBatchPredictOp in Vertex AI Pipelines is designed to handle batch predictions efficiently, and it can upload the prediction results directly to a BigQuery table. Additionally, the DataFlowPythonJobOp can be used to implement any required data processing logic within the DataFlow pipeline, making the pipeline reusable and streamlined for your production needs.
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You have recently used TensorFlow to train a classification model on tabular data, leveraging its capabilities to handle large datasets efficiently. You have created a DataFlow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. To integrate this model into your regular workflow, you need a solution for productionizing the model such that its predictions are automatically uploaded to a BigQuery table on a weekly schedule. What should you do?
A
Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataFlowPythonJobOp and the ModelBatchPredictOp components.
B
Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a DataFlow pipeline that reuses the data processing logic, sends requests to the endpoint, and then uploads predictions to a BigQuery table.
C
Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataFlowPythonJobOp and the ModelBatchPredictOp components.
D
Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines, create a pipeline that uses the BigQueryQueryJobOp and the BigQueryPredictModelJobOp components.