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

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: Sep 11, 2026

Q & A: 528 Questions and Answers

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2. Train Models & Run Experiments (25-30%):

  • Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
  • Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
  • Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
  • Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.

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

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