Professional-Machine-Learning-Engineer Exam Dumps | You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model

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Question 96/139

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 BigQuerys scheduling service to run the model retraining query periodically.

B. Create a pipeline in Vertex Al 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.

Correct Answer: B

BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data.
BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to perform linear regression on your data and create a model. BigQuery also provides a scheduling service that allows you to create and manage recurring SQL queries. You can use BigQuery's scheduling service to run the model retraining query periodically, such as every week. You can specify the destination table for the query results, and the schedule options, such as start date, end date, frequency, and time zone. You can also monitor the status and history of your scheduled queries. This solution can help you retrain the model on the cumulative data collected every week, while minimizing the development effort and the scheduling cost. References:
* BigQuery ML | Google Cloud
* Scheduling queries | BigQuery

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