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3. Models Management and Optimization (20-25%):

  • Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
  • Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.

What Certificate You Will Get by Passing DP-100

DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.

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

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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