Learn with Microsoft : DP-100 training material for 100% pass

Updated: Aug 05, 2026

No. of Questions: 528 Questions & Answers with Testing Engine

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Step 3: Taking Online Learning Paths

In addition, Microsoft recommends that the candidates for the DP-100 exam take online courses to adequately prepare for their test. You can find the following learning paths on the official certification webpage:

  • Create Machine Learning Models
  • Create No-Code Predictive Models with Azure Machine Learning
  • Build AI Solutions with Azure Machine Learning

Microsoft DP-100 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Passing Score:700
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure AI Engineer Associate
Exam Price:$165 USD
Real Exam Qty:40-60
Certificate Validity Period:1 year
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Italian, Russian, Arabic (Saudi Arabia), Indonesian (Indonesia)
Exam Format:Multiple choice, Multiple select, Drag and drop, Case studies, Yes/No
Exam Duration:100 minutes
Recommended Training:Microsoft Learn Learning Path
Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Exam Registration:Microsoft Learn Registration
Pearson VUE Scheduling
Sample Questions:Microsoft DP-100 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow)
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100

Microsoft DP-100: Career Prospects

After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:

  • Administrative Analyst
  • Data Analyst
  • Data Engineer
  • Software Developer

Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.

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

Microsoft DP-100 Exam Syllabus Topics:

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

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