You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach. What should you do?
A.
Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.
B.
Use Dataflow to preprocess the data. Write the output in TFRecord format to a Cloud Storage bucket.
C.
Write a query that preprocesses the data by using BigQuery. Export the query results as CSV files, and use those files to create a Vertex AI managed dataset.
D.
Use a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library. Export the data as CSV files, and use those files to create a Vertex AI managed dataset.
BigQuery integration with Vertex AI: BigQuery is fully integrated with Vertex AI, which means you can directly use BigQuery as a data source for Vertex AI managed datasets. By writing a query to preprocess the data and then creating a Vertex AI managed dataset from that query, you can skip extra steps like exporting or converting data into different formats. This is both efficient and leverages the native capabilities of the GCP platform
A
By writing a query that preprocesses the data using BigQuery and creating a new table, you can directly create a Vertex AI managed dataset with the new table as the data source. This approach is efficient because it leverages BigQuery’s powerful data processing capabilities and avoids the need to export data to another format or service. It also simplifies the process by keeping everything within the Google Cloud ecosystem. This makes it easier to manage and monitor your data and model training process.
The data is already in BigQuery. Preprocess the data in BigQuery. How is Dataflow easier than BigQuery? (question doesn't mention anything about scalability)
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