Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure

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Updated: Sep 19, 2026

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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.

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:	Drag and drop, Multiple select, Yes/No, Case studies, Multiple choice
Real Exam Qty:	40-60
Passing Score:	700
Exam Duration:	100 minutes
Certificate Validity Period:	1 year
Related Certifications:	Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate
Available Languages:	Russian, Korean, Chinese (Simplified), French, Italian, German, Spanish, Indonesian (Indonesia), Chinese (Traditional), Arabic (Saudi Arabia), English, Portuguese (Brazil), Japanese
Exam Price:	$165 USD
Recommended Training:	Microsoft Learn Learning Path Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Exam Registration:	Microsoft Learn Registration Pearson VUE Scheduling
Sample Questions:	Microsoft DP-100日本語 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow)
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100

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.

Microsoft DP-100日本語 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Optimize language models for AI applications	25-30%	- Optimize with Retrieval Augmented Generation 1. Prepare and process data 2. Create vector stores and indexes 3. Configure Azure AI Search - Evaluate and improve models 1. Optimize for accuracy and safety 2. Apply responsible generative AI 3. Test and evaluate responses - Implement generative AI solutions 1. Use Azure AI Foundry 2. Build prompt flows 3. Apply prompt engineering
Topic 2: Explore data and run experiments	20-25%	- Run experiments 1. Define parameters and configurations 2. Track runs with MLflow 3. Use automated machine learning 4. Configure experiment runs - Explore and visualize data 1. Detect anomalies and outliers 2. Profile and validate data 3. Identify features and relationships - Implement pipelines 1. Pass data between steps 2. Schedule and monitor pipelines 3. Create and publish pipelines 4. Build reusable components
Topic 3: Design and prepare a machine learning solution	20-25%	- Design a machine learning solution 1. Define compute specifications for workloads 2. Determine dataset structure and format 3. Plan model deployment requirements 4. Select development approach - Manage data assets 1. Select storage services 2. Create and maintain data assets 3. Register and manage datastores - Manage Azure Machine Learning workspace 1. Create and configure workspace 2. Use developer tools and CLI 3. Work with registries 4. Set up Git integration - Manage compute resources 1. Create and configure compute targets 2. Select environments 3. Attach and monitor compute
Topic 4: Train and deploy models	25-30%	- Deploy models 1. Deploy to batch endpoints 2. Deploy to online endpoints 3. Configure compute and scaling 4. Secure endpoints and manage access - Manage models 1. Register and version models 2. Package and validate models 3. Interpret models and explain predictions - Monitor and maintain models 1. Monitor performance and data drift 2. Update and retrain models 3. Implement MLOps practices - Train models 1. Apply responsible AI principles 2. Configure jobs and environments 3. Use HyperDrive for hyperparameter tuning 4. Run training scripts

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