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2. Train Models & Run Experiments (25-30%):
- Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
- Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
- Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
- Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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DP-100 Exam Target Audience
Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.
Step 4: Training with Practice Tests
Practicing more and more before taking the exam can help you score better. Using practice tests, you can also significantly improve your time management skills. You can easily find the relevant practice questions on different online learning platforms.
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Train and deploy models | 25-30% | - Monitor and maintain models
|
| Topic 2: Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Manage compute resources
|
| Topic 4: Explore data and run experiments | 20-25% | - Implement pipelines
|
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