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Answer: Azure Machine Learning Service
The question requires a data science environment that supports Caffe2/Chainer frameworks, works in both connected/disconnected network environments, handles datasets >20GB, and allows pipeline updates when reconnected. Azure Machine Learning Service (A) is the optimal choice because: 1) It supports custom frameworks like Caffe2 and Chainer through Docker environments and custom containers; 2) It enables local development and offline training using Azure ML SDK on personal devices; 3) It handles large datasets via integration with cloud storage and local caching; 4) It provides synchronization capabilities to update pipelines when reconnected. Azure Machine Learning Studio (B) is primarily web-based and requires network connectivity, making it unsuitable for disconnected environments. Azure Databricks (C) is cloud-native and requires persistent connectivity. Azure Kubernetes Service (D) is an orchestration platform, not a complete data science environment. The community discussion, particularly the highly upvoted comments (e.g., chaudha4 with 14 upvotes, Wayland with 10 upvotes), clarifies that Azure Machine Learning Service is the correct choice due to its hybrid capabilities and framework flexibility, despite some historical confusion with Studio.
Author: LeetQuiz Editorial Team
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You are planning to build a team data science environment for training machine learning models with datasets larger than 20 GB. The environment must meet the following requirements:
Which data science environment should you select?
A
Azure Machine Learning Service
B
Azure Machine Learning Studio
C
Azure Databricks
D
Azure Kubernetes Service (AKS)
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