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Exam DP-100: Designing and Implementing a Data Science Solution on Azure
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
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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.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 2: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 3: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 4: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
| Topic 5: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
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