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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

The dream of becoming a highly skilled data scientist can turn into a reality with the help of the Microsoft DP-100 exam. This exam tries to impart an associate-level understanding of data science and machine learning with an aim to generate a skilled workforce of data scientists.

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Microsoft DP-100 exam is designed for data scientists who want to demonstrate their ability to design and implement data science solutions on Microsoft Azure. DP-100 exam is an important step towards earning the Microsoft Certified: Azure Data Scientist Associate certification. It covers a range of topics including data exploration, data preparation, modeling, and deployment. Passing DP-100 exam requires a deep understanding of Azure services and tools, as well as strong data science skills.

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

Microsoft DP-100 Exam Syllabus Topics:

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

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