
Answer-first summary for fast verification
Answer: Azure Machine Learning Service
The question requires a data science environment that supports Caffe2/Chainer frameworks, works in both connected and disconnected network environments on personal devices, handles datasets over 20 GB, and allows pipeline updates when connected. Azure Machine Learning Service (A) is the correct choice because it provides SDK-based development that can run locally on personal devices (supporting offline work), supports custom frameworks like Caffe2 and Chainer through Docker environments, handles large datasets via integration with cloud storage and local caching, and enables pipeline synchronization when reconnected. Azure Machine Learning Studio (B) is primarily a web-based interface requiring constant network connectivity, making it unsuitable for disconnected environments. Azure Databricks (C) is cloud-native and requires network connectivity. Azure Kubernetes Service (D) is an orchestration platform, not a data science environment. The community discussion, including highly upvoted comments (e.g., 14 upvotes clarifying that Studio requires network access, 10 upvotes noting Service supports local compute), reinforces that A meets all requirements, while B, C, and D fail the disconnected environment criterion.
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 exceeding 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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