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Answer: 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.
The correct answer is 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.' 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. By keeping everything within the Google Cloud ecosystem, it simplifies the process of managing and monitoring your data and model training. This method takes advantage of the integration between BigQuery and Vertex AI, streamlining the workflow and reducing complexity.
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You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery, a fully managed data warehouse service. You need to preprocess and prepare the data before training the model, aiming for the simplest and most efficient approach within the Google Cloud ecosystem. 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.
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