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Microsoft DP-100 exam is a certification test that validates a candidate's knowledge and skills in designing and implementing data science solutions on Azure. DP-100 exam is ideal for data scientists, data engineers, and AI developers who want to showcase their expertise in building and deploying intelligent solutions using Azure services. The DP-100 exam focuses on the core concepts of data science, including data exploration and preparation, modeling, feature engineering, and machine learning.

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

The Microsoft DP-100 exam consists of approximately 40-60 multiple-choice questions and should be completed within 2 hours. DP-100 exam fee is $165 USD, and candidates can register for the exam through the Microsoft website. DP-100 exam is available in English, Chinese, Korean, and Japanese, making it accessible to a global audience.

The Microsoft DP-100 exam is designed to test the candidate's understanding of key concepts such as data exploration and preparation, modeling, deployment, and maintenance of machine learning models. It requires a deep understanding of Azure data services, including Azure Machine Learning, Azure Databricks, and Azure HDInsight. Furthermore, candidates are expected to have a good understanding of programming languages such as Python and R.

Candidates for the DP-100 certification exam should have experience with Azure services and tools, as well as a strong understanding of data science concepts and methodologies. It is recommended that you have at least one year of experience working with data science projects before attempting DP-100 exam. With the DP-100 certification, you can demonstrate your expertise to potential employers and advance your career in the field of data science.

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Microsoft DP-100 Exam Syllabus Topics:

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

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