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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.
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Microsoft Microsoft Azure DP-100-KR Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) Exams
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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:
- Create No-Code Predictive Models with Azure Machine Learning
- Create Machine Learning Models
- Build AI Solutions with Azure Machine Learning
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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 | Weight | Objectives |
|---|---|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 2: Explore data and train models | 35-40% | - Optimize model performance
|
| Topic 3: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Topic 4: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|







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