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Last Updated: Aug 12, 2026

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The price of the DP-100 exam is $165 USD.

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Exam Details & Skills Measured

Microsoft DP-100 is an associate-level certification exam. It can be taken as a proctored test if this option is available in your country. If it’s not, you can sit for it as an online exam at one of the Pearson VUE testing centers across the world. You should check the official webpage for more information about scheduling this exam.

When you are ready for this test, you have to pay $165 as a registration fee, which applies to a single delivery of the exam. You can take it in English, Korean, Japanese, or Simplified Chinese. Microsoft DP-100 contains 40-60 questions that cover different types, such as drag and drop, multiple choice, active screen, build list, short answer, best answer, and case studies, among others. If you want to pass this test on the first try, you should get 720 points on a scale of 100-1000.

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Conclusion

Data science is a lucrative career option and you can explore it without any hassle if you’ve managed to taste success in the Microsoft DP-100 exam, which is known to create a skilled workforce of Azure data scientists. However, a great outcome in such a test will only come if the aspirant is referring to the updated and recent study resources like the official courses provided by the exam vendor itself.

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Knowing Associated Certification

The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 exam will also help the test-taker to gain some ACE college credits.

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

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Microsoft DP-100 Exam Syllabus Topics:

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

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