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This exam will provide substantial career growth for data scientist-related job roles. If anyone’s job profile demands natural language processing and predictive analysis, s/he can aim at this exam to access needed expertise.

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

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  • Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure 1st Edition by Pierstefano Tucci
  • Designing and Implementing a Data Science Solution on Azure: Prepare for exam DP-100 by Stavros Koureas
  • Designing and Implementing a Data Science Solution on Azure - Exam Prep: Microsoft Azure DP-100 Certification Exam | Most Unique & Latest Questionnaires by VB Dev
  • How I Passed DP-100: Designing and Implementing a Data Science Solution on Azure: Sure Shot Certification Tips by Empirical Matt Publications
  • Learn How to Pass DP-100: Designing and Implementing a Data Science Solution on Azure Exam: Learn at your fingertips by Rodrezil Publications

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Obligatory Prerequisites

Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.

Microsoft DP-100日本語 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. 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. Track runs with MLflow
  • 3. Configure experiment runs
  • 4. Define parameters and configurations
- Implement pipelines
  • 1. Pass data between steps
  • 2. Build reusable components
  • 3. Create and publish pipelines
  • 4. Schedule and monitor pipelines
- Explore and visualize data
  • 1. Identify features and relationships
  • 2. Profile and validate data
  • 3. Detect anomalies and outliers
Train and deploy models25-30%- Manage models
  • 1. Interpret models and explain predictions
  • 2. Register and version models
  • 3. Package and validate models
- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Implement MLOps practices
  • 3. Monitor performance and data drift
- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Apply responsible AI principles
  • 3. Configure jobs and environments
  • 4. Run training scripts
- Deploy models
  • 1. Deploy to batch endpoints
  • 2. Secure endpoints and manage access
  • 3. Configure compute and scaling
  • 4. Deploy to online endpoints
Design and prepare a machine learning solution20-25%- 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. Create and configure workspace
  • 3. Set up Git integration
  • 4. Use developer tools and CLI
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Create and configure compute targets
  • 3. Select environments
- Design a machine learning solution
  • 1. Define compute specifications for workloads
  • 2. Determine dataset structure and format
  • 3. Plan model deployment requirements
  • 4. Select development approach

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