Microsoft DP-100: Requirements
The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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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
For more info visit:
Microsoft DP-100 Exam Reference
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Skills measured
- Run experiments and train models (20–25%)
- Implement responsible machine learning (5–10%)
- Manage Azure resources for machine learning (25–30%)
- Deploy and operationalize machine learning solutions (35–40%)
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads - Manage compute and data assets |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
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