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How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-100&examname=Exam%20DP-100:%20Designing%20and%20Implementing%20a%20Data%20Science%20Solution%20on%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-100

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:

  • Create Machine Learning Models
  • Create No-Code Predictive Models with Azure Machine Learning
  • Build AI Solutions with Azure Machine Learning

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Microsoft DP-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Design and prepare a machine learning solution20-25%- Manage Azure Machine Learning workspace
  • 1. Work with registries
  • 2. Use developer tools and CLI
  • 3. Set up Git integration
  • 4. Create and configure workspace
- Manage compute resources
  • 1. Select environments
  • 2. Create and configure compute targets
  • 3. Attach and monitor compute
- Design a machine learning solution
  • 1. Select development approach
  • 2. Define compute specifications for workloads
  • 3. Determine dataset structure and format
  • 4. Plan model deployment requirements
- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 3. Register and manage datastores
Explore data and run experiments20-25%- Run experiments
  • 1. Track runs with MLflow
  • 2. Configure experiment runs
  • 3. Use automated machine learning
  • 4. Define parameters and configurations
- Implement pipelines
  • 1. Create and publish pipelines
  • 2. Schedule and monitor pipelines
  • 3. Build reusable components
  • 4. Pass data between steps
- Explore and visualize data
  • 1. Detect anomalies and outliers
  • 2. Identify features and relationships
  • 3. Profile and validate data
Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Apply prompt engineering
  • 2. Use Azure AI Foundry
  • 3. Build prompt flows
- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Prepare and process data
  • 3. Create vector stores and indexes
- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Test and evaluate responses
  • 3. Optimize for accuracy and safety
Train and deploy models25-30%- Manage models
  • 1. Interpret models and explain predictions
  • 2. Package and validate models
  • 3. Register and version models
- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Configure jobs and environments
  • 3. Apply responsible AI principles
  • 4. Run training scripts
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Update and retrain models
  • 3. Implement MLOps practices
- Deploy models
  • 1. Deploy to batch endpoints
  • 2. Secure endpoints and manage access
  • 3. Deploy to online endpoints
  • 4. Configure compute and scaling

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