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

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

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

Автор: Dayne Sorvisto
Дата выхода: 2025
Издательство: Microsoft Press
Количество страниц: 192
Размер файла: 12,5 МБ
Тип файла: EPUB
Добавил: codelibs
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 Prepare for Microsoft Exam DP-100 and demonstrate your real-world knowledge of managing data ingestion and preparation, model training and deployment, and Machine Learning solution monitoring with Python, Azure Machine Learning, and MLflow. Designed for professionals with Data Science experience, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Scientist Associate level.

 This book is written for IT professionals who intend to take the DP-100 exam as well as data engineers, data scientists, and other data professionals who want to learn to design and implement a Data Science solution in Azure. In addition to the exam material, the book is meant to enrich your knowledge of Azure Machine Learning by using it to implement Machine Learning operations in Azure and to design end-to-end Data Science solutions.

 This book covers every major topic area found on the exam, but it does not cover every exam question. Only the Microsoft exam team has access to the exam questions, and Microsoft regularly adds new questions to the exam, making it impossible to cover specific questions. You should consider this book a supplement to your relevant real-world experience and other study materials. If you encounter a topic in this book that you do not feel completely comfortable with, use the “Need more review?” links you’ll find in the text to find more information and take the time to research and study the topic.

 Focus on the expertise measured by these objectives:

  • Design and prepare a Machine Learning solution
  • Explore data and train models
  • Prepare a model for deployment
  • Deploy and retrain a model

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