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Updated: Sep 07, 2026

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Skills Covered

To nail DP-100, you will need to scrutinize the below-mentioned areas:

  • Execute Experiments & Train Models

    This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.

  • Set up Azure ML Workspace

    The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.

  • Manage and Optimize Models

    Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.

  • Deploy and Consume Models

    The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.

3. Models Management and Optimization (20-25%):

  • 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.
  • 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.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • 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.

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

Microsoft DP-100日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Exam Format:Case Study, Lab, Multiple Choice, Drag and Drop, Interactive Tasks
Available Languages:Spanish, English, Chinese (Simplified), Korean, Japanese, German, French, Portuguese
Exam Duration:100 minutes
Certificate Validity Period:1 year
Exam Price:$165 USD
Related Certifications:Microsoft Certified: Azure Data Scientist Associate
Passing Score:700/1000
Real Exam Qty:40-60
Sample Questions:Microsoft DP-100日本語 Sample Questions
Exam Way:Online proctored exam or test center delivery through Pearson VUE.
Pre Condition:Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/

Microsoft DP-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and retrain models10-15%- Monitor deployed models
  • 1. Monitor model performance
  • 2. Track data drift
- Implement retraining pipelines
  • 1. Create scheduled retraining workflows
  • 2. Manage ML pipelines
Topic 2: Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Configure security and access
  • 2. Manage compute resources
  • 3. Configure workspace resources
- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
Topic 3: Explore data and train models35-40%- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
- Prepare data for modeling
  • 1. Ingest and transform data
  • 2. Manage datasets and datastores
- Run experiments and train models
  • 1. Track experiments
  • 2. Use automated machine learning
  • 3. Perform hyperparameter tuning
Topic 4: Prepare a model for deployment20-25%- Manage deployment assets
  • 1. Create inference configurations
  • 2. Register models
- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints

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