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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:
| Schedule Exam | Pearson VUE |
| Exam Name | Microsoft Certified - Azure Data Scientist Associate |
| Exam Code | DP-100 |
| Exam Price | $165 (USD) |
| Number of Questions | 40-60 |
| Sample Questions | Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions |
| Books / Training | DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Passing Score | 700 / 1000 |
| Duration | 120 mins |
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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%):
- 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.
- 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.
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 2: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 3: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| 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: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
