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

   Author: creativelivenew1   |   05 August 2025   |   Comments icon: 0


Free Download Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure by Dayne Sorvisto
English | November 1, 2024 | ISBN: 0135350603 | 192 pages | MOBI | 12 Mb
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.


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 modelThis Microsoft Exam Ref:Organizes its coverage by exam objectivesFeatures strategic, what-if scenarios to challenge youAssumes you have experience in designing and creating a suitable working environment for data science workloads, training machine learning models, and managing, deploying, and monitoring scalable machine learning solutionsAbout the Exam
Exam DP-100 focuses on knowledge needed to design and prepare a machine learning solution, manage an Azure Machine Learning workspace, explore data and train models, create models by using the Azure Machine Learning designer, prepare a model for deployment, manage models in Azure Machine Learning, deploy and retrain a model, and apply machine learning operations (MLOps) practices.
About Microsoft Certification
Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating your expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, including knowledge and experience using Azure Machine Learning and MLflow.

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