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Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.
Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.
A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.
Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate
Microsoft DP-100 Exam Reference
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Passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is the major requirement for earning the Microsoft Certified: Azure Data Scientist Associate certification. This test measures the ability of the professionals to execute the following technical tasks: setting up an Azure Machine Learning workspace; running experiments & train models; optimizing and handling models; deploying and consuming models.
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| Section | Objectives |
|---|---|
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
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