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Answer: Use BigQuery’s scheduling service to run the model retraining query periodically.
The correct answer is A. Using BigQuery’s scheduling service to run the model retraining query periodically minimizes both development effort and scheduling cost. The scheduling feature is built into BigQuery, eliminating the need for creating additional pipelines, functions, or workflows. It also provides straightforward configuration, making it easy to set up a query schedule within the BigQuery interface. This approach avoids the extra configuration, complexity, and potential costs associated with the other options.
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As a machine learning engineer, you have developed a model using BigQuery ML for linear regression to predict key business metrics. Your model's accuracy relies on being retrained with cumulative data collected on a weekly basis. Considering you want to minimize both development effort and scheduling costs, what is the most effective approach to schedule the retraining of your model?
A
Use BigQuery’s scheduling service to run the model retraining query periodically.
B
Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.
C
Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
D
Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.
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