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

DP-100日本語 pass collection

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

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

Ideal Candidate Profile

This exam will provide substantial career growth for data scientist-related job roles. If anyone’s job profile demands natural language processing and predictive analysis, s/he can aim at this exam to access needed expertise.

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How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-100&examname=Exam%20DP-100:%20Designing%20and%20Implementing%20a%20Data%20Science%20Solution%20on%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-100

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

Microsoft DP-100日本語 Exam Syllabus Topics:

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

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