Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Important

The content of this exam was updated on May 20, 2021. Please download the skills measured document below to see what changed.

Candidates for this exam should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Microsoft Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. They run data experiments and train predictive models. In addition, they manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

You may be eligible for ACE college credit if you pass this certification exam. See ACE college credit for certification exams for details.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

Related exams: none

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  • DP-100: Designing and Implementing a Data Science Solution on Azure

    Languages: English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, Indonesian (Indonesia)

    Retirement date: none

    This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.

    Price based on the country or region in which the exam is proctored.

    for Designing and Implementing a Data Science Solution on Azure

    All objectives of the exam are covered in depth so you'll be ready for any question on the exam.

Skills measured

  • The content of this exam was updated on May 20, 2021. Please download the exam skills outline below to see what changed.
  • Manage Azure resources for machine learning (25–30%)
  • Run experiments and train models (20–25%)
  • Deploy and operationalize machine learning solutions (35–40%)
  • Implement responsible machine learning (5–10%)

Download exam skills outline

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** Complete this exam before the retirement date to ensure it is applied toward your certification. After the retirement date, please refer to the related certification for exam requirements.