Google Professional Machine Learning Engineer
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In the context of optimizing a distributed machine learning system for a global e-commerce platform, where the system is required to process petabytes of data across multiple data centers with minimal latency and cost, which of the following techniques is MOST effective for minimizing communication overhead between nodes? Consider the need for real-time model updates and the constraints of network bandwidth and data privacy regulations. Choose the best option.
In the context of optimizing a distributed machine learning system for a global e-commerce platform, where the system is required to process petabytes of data across multiple data centers with minimal latency and cost, which of the following techniques is MOST effective for minimizing communication overhead between nodes? Consider the need for real-time model updates and the constraints of network bandwidth and data privacy regulations. Choose the best option.
Real Exam
A
Data replication across all nodes to ensure high availability and fault tolerance, despite the potential increase in communication overhead.
15.8%
B
Model compression techniques such as pruning and quantization to reduce the size of the model, without directly addressing the communication overhead.
0.0%
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