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Discuss the implications of model synchronization in a distributed machine learning environment. How does Spark ML ensure that all nodes have the most up-to-date model parameters during training?
A
Spark ML uses a master-slave architecture where the master node updates all slave nodes.
B
Spark ML employs a peer-to-peer synchronization method for model parameters.
C
Spark ML utilizes a centralized parameter server to distribute updates to all nodes.
D
Spark ML does not support model synchronization; each node trains independently.
