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Microsoft DP-100 certification exam is designed to test the skills of data professionals who want to design and implement data science solutions on the Azure platform. DP-100 exam is intended for individuals who have experience working with Azure data services and are familiar with data science concepts and techniques. DP-100 exam covers a wide range of topics, including data exploration and preparation, modeling, deployment, and monitoring.
Microsoft DP-100 certification exam is designed to test the candidate's knowledge and skills in designing and implementing a data science solution on Azure. DP-100 exam is aimed at data scientists, data analysts, and other professionals who work with data and want to demonstrate their proficiency in using Azure tools and services to build scalable and efficient data solutions.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
The DP-100 exam covers a wide range of topics, including data exploration and preparation, modeling, visualization, and deployment. Candidates will be tested on their ability to use various Azure tools and services, such as Azure Machine Learning, Azure Databricks, and Azure Cognitive Services, to build end-to-end data science solutions. They will also need to demonstrate their understanding of the principles of machine learning, statistical analysis, and data visualization, as well as their ability to communicate insights and findings to stakeholders. Passing DP-100 exam is a significant achievement for data science professionals who want to showcase their expertise in Azure-based data science solutions.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Design and prepare a machine learning solution | - Manage compute and data assets - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace |







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