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

DP-100日本語 Exam Simulator
  • Exam Code: DP-100J
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Aug 03, 2026
  • Q & A: 528 Questions and Answers
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3. Models Management and Optimization (20-25%):

  • Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
  • Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
  • Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.

Microsoft DP-100: Career Prospects

After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:

  • Administrative Analyst
  • Data Engineer
  • Data Analyst
  • Software Developer

Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.

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Knowing Associated Certification

The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 exam will also help the test-taker to gain some ACE college credits.

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
Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Create and maintain data assets
  • 2. Select storage services
  • 3. Register and manage datastores
- 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. Create and configure compute targets
  • 3. Attach and monitor compute
- 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
Optimize language models for AI applications25-30%- 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
- Optimize with Retrieval Augmented Generation
  • 1. Prepare and process data
  • 2. Create vector stores and indexes
  • 3. Configure Azure AI Search
Explore data and run experiments20-25%- Run experiments
  • 1. Define parameters and configurations
  • 2. Use automated machine learning
  • 3. Configure experiment runs
  • 4. Track runs with MLflow
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Identify features and relationships
  • 3. Detect anomalies and outliers
- Implement pipelines
  • 1. Pass data between steps
  • 2. Create and publish pipelines
  • 3. Schedule and monitor pipelines
  • 4. Build reusable components
Train and deploy models25-30%- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Configure jobs and environments
  • 3. Run training scripts
  • 4. Apply responsible AI principles
- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
- Deploy models
  • 1. Secure endpoints and manage access
  • 2. Deploy to online endpoints
  • 3. Deploy to batch endpoints
  • 4. Configure compute and scaling
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Implement MLOps practices
  • 3. Update and retrain models

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