nerdexam
Microsoft

DP-100 · Question #36

You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size. You have the following requirements: - Models must be built…

The correct answer is A. Azure Machine Learning Service. The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning. Incorrect Answers: B: Use Machine Learning Studio when…

Design and prepare a machine learning solution

Question

You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size. You have the following requirements: - Models must be built using Caffe2 or Chainer frameworks. - Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments. - Personal devices must support updating machine learning pipelines when connected to a network. You need to select a data science environment. Which environment should you use?

Options

  • AAzure Machine Learning Service
  • BAzure Machine Learning Studio
  • CAzure Databricks
  • DAzure Kubernetes Service (AKS)

How the community answered

(53 responses)
  • A
    85% (45)
  • B
    4% (2)
  • C
    4% (2)
  • D
    8% (4)

Explanation

The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning. Incorrect Answers: B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions. https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

Topics

#Azure Machine Learning Service#Hybrid ML Development#ML Environment Selection#Deep Learning

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