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

  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Jul 20, 2026
  • Q&As: 528 Questions and Answers

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About Microsoft DP-100日本語 Real Exam

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DP-100日本語 exam dumps

Basic Exam Traits

The Microsoft DP-100 is an associate-level job role-based exam. Its structure is the same as any other exam falling in this category. As per the standard test format, DP-100 exam is likely to contain 40-60 exam questions. As far as question format is concerned, Microsoft doesn’t follow a set pattern. The exam is likely to cover questions based on the MCQ pattern. However, the odds of including items based on other patterns like case studies and best answers are also high. What’s more, there is no exact passing score as there is no fixed number of questions and it might change as per the final number of tasks. Nonetheless, a test-taker must secure 70% passing marks to be called successful in the official exam. Currently, this test can be taken in English, Japanese, Chinese (Simplified), and Korean worldwide. The standard exam fee is $165 and is likely to get changed as per the location of the test-taker.

The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.

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

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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