Microsoft DP-100: Preparation Process
Thorough preparation is an important part of the Microsoft DP-100 exam and its associate-level certification. Here is the ultimate guide to prepare for this test with great deliberation.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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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
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Microsoft DP-100 Exam Reference
2. Train Models & Run Experiments (25-30%):
- 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.
- 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.
- 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.
- 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.
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Topic 2: Explore data and run experiments | 20-25% | - Run experiments
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Manage Azure Machine Learning workspace
|
| Topic 4: Train and deploy models | 25-30% | - Monitor and maintain models
|




