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%):
- 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.
- 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.
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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100: Career Prospects
After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:
- Data Analyst
- Data Engineer
- Software Developer
- Administrative Analyst
Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| 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 |




