Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
You are working on a project that requires executing batch predictions on a dataset of 100 million records stored in a BigQuery table using a custom TensorFlow DNN regressor model. The project has strict requirements for minimizing infrastructure complexity and operational overhead, while ensuring that the predicted results are efficiently stored back into a BigQuery table. Additionally, the solution must be cost-effective and scalable to accommodate potential increases in data volume. Considering these constraints, which approach should you take? (Choose one correct option)
You are working on a project that requires executing batch predictions on a dataset of 100 million records stored in a BigQuery table using a custom TensorFlow DNN regressor model. The project has strict requirements for minimizing infrastructure complexity and operational overhead, while ensuring that the predicted results are efficiently stored back into a BigQuery table. Additionally, the solution must be cost-effective and scalable to accommodate potential increases in data volume. Considering these constraints, which approach should you take? (Choose one correct option)
Real Exam