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The DP-100 exam is an excellent opportunity for data scientists and engineers to validate their skills and gain a competitive advantage in the job market. Designing and Implementing a Data Science Solution on Azure certification provides an added advantage for organizations to identify skilled data scientists and engineers who can design and implement data science solutions on Azure.
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What Certificate You Will Get by Passing DP-100
DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 certification exam is designed to test your knowledge and skills in designing and implementing data science solutions using Azure services. Designing and Implementing a Data Science Solution on Azure certification is intended for individuals who work with data scientists, data engineers, and other stakeholders to develop and deploy machine learning (ML) and artificial intelligence (AI) solutions.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Optimize and manage models | - Improve model performance
- Track experiments and manage model lifecycle
|
| Topic 2: Explore and analyze data | - Ingest and prepare data for modeling
- Perform exploratory data analysis
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| Topic 3: Train machine learning models | - Tune hyperparameters and evaluate models
- Train models using Azure Machine Learning
|
| Topic 4: Design and prepare a machine learning solution | - Manage compute and data assets
- Plan and configure Azure Machine Learning workspace
- Select appropriate Azure services for machine learning workloads
|
| Topic 5: Deploy and consume models | - Monitor deployed models and endpoints
- Deploy models to endpoints
|