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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
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Who should take the DP-100 exam
The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled as a Microsoft Certified Azure Data Scientist Associate. If candidates want significant improvement in career growth needs enhanced knowledge, skills, and talents. The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam then he should take this exam.
The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:
1. Azure ML Workspace Set-Up (30-35%):
- Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
- Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
- Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.
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Certification Path
The Microsoft Certified Azure Data Scientist Associate Certification includes only one DP-100 Exam.
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 2: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 3: 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 4: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 5: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
