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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
Target Audience & Requirements
The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| 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 |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
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