PDF Version DemoStep 1: Reviewing the Exam Topics
The best way to start the preparation process is to visit the official website of the certification exam. The official site is the most reliable source for the right information regarding the test. It is recommended that you download a detailed list of the exam objectives from the webpage and thoroughly study it before attempting the actual test.
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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Step 3: Taking Online Learning Paths
In addition, Microsoft recommends that the candidates for the DP-100 exam take online courses to adequately prepare for their test. You can find the following learning paths on the official certification webpage:
- Build AI Solutions with Azure Machine Learning
- Create No-Code Predictive Models with Azure Machine Learning
- Create Machine Learning Models
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
| Topic 2: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 3: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 4: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 5: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
