Databricks Certified Machine Learning - Associate
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Explain why parallelizing sequential/iterative models can be difficult. Provide an example of a model that is difficult to parallelize and explain the challenges involved.
A
Parallelizing sequential/iterative models can be difficult because the model's training process involves multiple iterations, and each iteration depends on the results of the previous iteration. This dependency makes it challenging to distribute the training process across multiple compute resources.
B
Parallelizing sequential/iterative models can be easy because the model's training process involves multiple iterations, and each iteration can be executed independently of the previous iteration. This independence allows the training process to be distributed across multiple compute resources without any challenges.
C
Parallelizing sequential/iterative models can be difficult because the model's training process involves multiple iterations, but each iteration can be executed independently of the previous iteration. This independence allows the training process to be distributed across multiple compute resources without any challenges.
D
Parallelizing sequential/iterative models can be easy because the model's training process does not involve multiple iterations, and each iteration can be executed independently of the previous iteration. This independence allows the training process to be distributed across multiple compute resources without any challenges.