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

  • Exam Code: DP-100
  • Exam Name: Designing and Implementing a Data Science Solution on Azure
  • Updated: Sep 14, 2026   Q&As: 528 Questions and Answers
  • Q & A: 528 Questions and Answers

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

Microsoft DP-100: Requirements

The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:
Schedule ExamPearson VUE
Books / TrainingDP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Number of Questions40-60
Exam Price$165 (USD)
Exam NameMicrosoft Certified - Azure Data Scientist Associate
Exam CodeDP-100
Sample QuestionsMicrosoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Duration120 mins
Passing Score700 / 1000
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Microsoft DP-100 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Explore data and run experiments20-25%- Run experiments
  • 1. Use automated machine learning
  • 2. Track runs with MLflow
  • 3. Define parameters and configurations
  • 4. Configure experiment runs
- Implement pipelines
  • 1. Build reusable components
  • 2. Pass data between steps
  • 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
Topic 2: Optimize language models for AI applications25-30%- 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. Optimize for accuracy and safety
  • 3. Test and evaluate responses
- Implement generative AI solutions
  • 1. Apply prompt engineering
  • 2. Use Azure AI Foundry
  • 3. Build prompt flows
Topic 3: Train and deploy models25-30%- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Run training scripts
  • 3. Configure jobs and environments
  • 4. Apply responsible AI principles
- Manage models
  • 1. Interpret models and explain predictions
  • 2. Register and version models
  • 3. Package and validate models
- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Monitor performance and data drift
  • 3. Update and retrain models
- Deploy models
  • 1. Configure compute and scaling
  • 2. Secure endpoints and manage access
  • 3. Deploy to batch endpoints
  • 4. Deploy to online endpoints
Topic 4: Design and prepare a machine learning solution20-25%- Design a machine learning solution
  • 1. Define compute specifications for workloads
  • 2. Select development approach
  • 3. Plan model deployment requirements
  • 4. Determine dataset structure and format
- Manage data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
- Manage Azure Machine Learning workspace
  • 1. Work with registries
  • 2. Set up Git integration
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Create and configure compute targets
  • 3. Select environments

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