Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) : DP-100日本語

DP-100日本語 real exams

Exam Code: DP-100J

Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)

Updated: Aug 14, 2026

Q & A: 528 Questions and Answers

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About Microsoft DP-100日本語 Exam

Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:

Duration120 mins
Exam NameMicrosoft Certified - Azure Data Scientist Associate
Passing Score700 / 1000
Exam CodeDP-100
Sample QuestionsMicrosoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Schedule ExamPearson VUE
Books / TrainingDP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Exam Price$165 (USD)
Number of Questions40-60

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

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What Certificate You Will Get by Passing DP-100

DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.

Microsoft DP-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Design and prepare a machine learning solution20-25%- Manage Azure Machine Learning workspace
  • 1. Work with registries
  • 2. Set up Git integration
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
- Manage compute resources
  • 1. Attach and monitor compute
  • 2. Create and configure compute targets
  • 3. Select environments
- 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. Determine dataset structure and format
  • 4. Define compute specifications for workloads
Explore data and run experiments20-25%- Implement pipelines
  • 1. Pass data between steps
  • 2. Create and publish pipelines
  • 3. Schedule and monitor pipelines
  • 4. Build reusable components
- Run experiments
  • 1. Configure experiment runs
  • 2. Track runs with MLflow
  • 3. Define parameters and configurations
  • 4. Use automated machine learning
- 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%- Implement generative AI solutions
  • 1. Use Azure AI Foundry
  • 2. Apply prompt engineering
  • 3. Build prompt flows
- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Test and evaluate responses
  • 3. Optimize for accuracy and safety
- Optimize with Retrieval Augmented Generation
  • 1. Prepare and process data
  • 2. Create vector stores and indexes
  • 3. Configure Azure AI Search
Train and deploy models25-30%- Manage models
  • 1. Interpret models and explain predictions
  • 2. Register and version models
  • 3. Package and validate models
- 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. Monitor performance and data drift
  • 2. Implement MLOps practices
  • 3. Update and retrain models
- Deploy models
  • 1. Secure endpoints and manage access
  • 2. Configure compute and scaling
  • 3. Deploy to online endpoints
  • 4. Deploy to batch endpoints

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