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Certification Path

The Microsoft Certified Azure Data Scientist Associate Certification includes only one DP-100 Exam.

Target Audience & Requirements

The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.

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

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Passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is the major requirement for earning the Microsoft Certified: Azure Data Scientist Associate certification. This test measures the ability of the professionals to execute the following technical tasks: setting up an Azure Machine Learning workspace; running experiments & train models; optimizing and handling models; deploying and consuming models.

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Microsoft DP-100 Exam Syllabus Topics:

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

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