You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
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.
No additional setup: BigQuery's scheduling feature is built-in, eliminating the need to create pipelines, functions, or workflows.
Straightforward configuration: Setting up a schedule for a query is a simple process within the BigQuery interface.
A is right
Using BigQuery's scheduling service allows you to automate the retraining process without needing to write custom code or manage additional dependencies.
Option B: Vertex AI Pipelines offer flexibility for complex workflows, but it involves more development effort and potential costs for pipeline execution.
Option C: Cloud Functions provide a serverless way to execute code, but they incur execution costs and require additional configuration for triggering and permissions.
Option D: Workflows can manage complex orchestration, but configuring the BigQuery API Connector and Cloud Scheduler adds complexity and potential costs.
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