Microsoft Designing and Implementing a Data Science Solution on Azure : DP-100

DP-100 real exams

Exam Code: DP-100

Exam Name: Designing and Implementing a Data Science Solution on Azure

Updated: Jul 29, 2026

Q & A: 528 Questions and Answers

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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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Microsoft DP-100 Exam Syllabus Topics:

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