Exam Ref DP-100: Designing and Implementing a Data Science Solution on Azure
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Title Page....2 Copyright Page....3 Contents at a glance....8 Contents....9 Acknowledgments....14 About the Author....15 Introduction....16 Organization of this book....17 Preparing for the exam....17 Microsoft certifications....18 Access the Exam Updates chapter and online references....18 Errata, updates, & book support....19 Stay in touch....20 Chapter 1. Design and prepare a machine learning solution....21 Skill 1.1: Design a machine learning solution....23 Skill 1.2: Manage an Azure Machine Learning workspace....49 Skill 1.3: Manage data in an Azure Machine Learning workspace....65 Skill 1.4: Manage compute for experiments in Azure Machine Learning....76 Chapter summary....94 Thought experiment....95 Thought experiment answers....96 Chapter 2. Explore data and train models....98 Skill 2.1: Explore data by using data assets and datastores....99 Skill 2.2: Create models by using the Azure Machine Learning Designer....110 Skill 2.3: Use automated machine learning to explore optimal models....123 Skill 2.4: Use notebooks for custom model training....152 Skill 2.5: Tune hyperparameters with Azure Machine Learning....165 Chapter summary....171 Thought experiment....172 Thought experiment answers....173 Chapter 3. Prepare a model for deployment....175 Skill 3.1: Run model training scripts....175 Skill 3.2: Implement training pipelines....204 Skill 3.3: Manage models in Azure Machine Learning....236 Chapter summary....247 Thought experiment....248 Thought experiment answers....249 Chapter 4. Deploy and retrain a model....252 Skill 4.1: Deploy a model....255 Skill 4.2: Apply machine learning operations (MLOps) practices....297 Chapter summary....332 Thought experiment....333 Thought experiment answers....334 Chapter 5. Exam DP-100: Designing and Implementing a Data Science Solution on Azure—updates....336 The purpose of this chapter....336 News and commentary about the exam objective updates....339 Updated technical content....339 Objective mapping....339 Index....346 Code Snippets....382
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Ниже — практический обзор по теме «exam».
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.
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. This book covers every major topic area found on the exam, but it does not cover every exam question. 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 solutionExplore data and train modelsPrepare a model for deploymentDeploy and retrain a model
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автор — Sorvisto Dayne, издательство Microsoft Press, год выпуска 2025, 410 страниц.
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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.
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