PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #300
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #300: Real Exam Question with Answer & Explanation
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Question
You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex AI Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?
Options
- APreprocess and stage the data in BigQuery prior to feeding it to the model during training and
- BUse the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the
- CCreate a component in the Vertex AI Pipelines directed acyclic graph (DAG) to calculate the
- DCreate SQL queries to calculate and store the required statistics in separate BigQuery tables that
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