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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:

Exam CodeDP-100
Exam Price$165 (USD)
Schedule ExamPearson VUE
Sample QuestionsMicrosoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Exam NameMicrosoft Certified - Azure Data Scientist Associate
Number of Questions40-60
Duration120 mins
Books / TrainingDP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Passing Score700 / 1000

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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