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Skills measured
- Implement responsible machine learning (5–10%)
- Deploy and operationalize machine learning solutions (35–40%)
- Manage Azure resources for machine learning (25–30%)
- Run experiments and train models (20–25%)
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.
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How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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.
- 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.
- 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.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
|
| Topic 2: Explore data and run experiments | 20-25% | - Run experiments
|
| Topic 3: Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
| Topic 4: Train and deploy models | 25-30% | - Deploy models
|
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