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Your company is currently utilizing TensorFlow for training and deploying several machine learning models on-premises. However, you're facing significant challenges in managing the high costs associated with training sessions and the complexity of updating models. To address these issues, you're evaluating the use of Vertex Pipelines and Kubeflow Pipelines. Specifically, you're considering whether starting with Kubeflow Pipelines would allow for a future transition to Vertex AI, which offers a more automated and managed environment. Given this scenario, which of the following statements accurately describe the compatibility and features of Kubeflow Pipelines and Vertex Pipelines? (Choose 4 options)
A
Kubeflow Pipelines and Vertex Pipelines are fundamentally incompatible, making migration between them impossible.
B
Kubeflow Pipelines written with the DSL (Domain Specific Language) can be seamlessly used within Vertex AI.
C
Kubeflow Pipelines are designed to work exclusively within the Google Cloud Platform (GCP) environment.
D
Kubeflow Pipelines can be deployed and operated in any environment that supports Kubernetes, not limited to GCP.
E
Kubeflow Pipelines have the capability to utilize Kubernetes persistent volume claims (PVC) for data persistence.
F
Vertex Pipelines support the use of Cloud Storage FUSE, allowing Cloud Storage buckets to be mounted as file systems.