Google Professional Machine Learning Engineer
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You are leading a project for a major banking institution that involves developing a deep neural network model using TensorFlow. The model's architecture is highly complex, requiring processing of massive datasets with intricate matrix computations. Currently, the training jobs are being executed on a cluster of virtual machines, but the duration of these jobs has extended to several weeks, threatening the project's deadline. The banking institution has emphasized the importance of compliance with financial regulations, cost-effectiveness, and the ability to scale resources dynamically based on the workload. Considering these constraints, what is the most effective solution to accelerate the training jobs while adhering to the project's requirements? (Choose one correct option)
You are leading a project for a major banking institution that involves developing a deep neural network model using TensorFlow. The model's architecture is highly complex, requiring processing of massive datasets with intricate matrix computations. Currently, the training jobs are being executed on a cluster of virtual machines, but the duration of these jobs has extended to several weeks, threatening the project's deadline. The banking institution has emphasized the importance of compliance with financial regulations, cost-effectiveness, and the ability to scale resources dynamically based on the workload. Considering these constraints, what is the most effective solution to accelerate the training jobs while adhering to the project's requirements? (Choose one correct option)