Microsoft DP-100 Exam Details & Actual Exam Questions

Exam Code/Number: DP-100
Exam Name/Title: Designing and Implementing a Data Science Solution on Azure
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure

Exam Questions: 528
Updated On: Jul,23 2026
Certification Level: Associate

Microsoft Designing and Implementing a Data Science Solution on Azure Exam Questions

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Microsoft DP-100 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Designing and Implementing a Data Science Solution on Azure
Exam Number:	DP-100
Exam Duration:	100 minutes
Exam Price:	$165 USD
Available Languages:	English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese
Related Certifications:	Microsoft Certified: Azure Data Scientist Associate
Certificate Validity Period:	1 year
Real Exam Qty:	40-60
Passing Score:	700/1000
Exam Format:	Multiple Choice, Case Study, Drag and Drop, Lab, Interactive Tasks
Sample Questions:	Microsoft DP-100 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 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Explore data and train models	35-40%	- Run experiments and train models 1. Use automated machine learning 2. Track experiments 3. Perform hyperparameter tuning - Prepare data for modeling 1. Ingest and transform data 2. Manage datasets and datastores - Optimize model performance 1. Evaluate models 2. Improve accuracy and performance
Topic 2: Deploy and retrain models	10-15%	- 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: Prepare a model for deployment	20-25%	- Deploy machine learning models 1. Deploy real-time inference endpoints 2. Deploy batch inference pipelines - Manage deployment assets 1. Create inference configurations 2. Register models
Topic 4: Design and prepare a machine learning solution	20-25%	- Design an Azure Machine Learning workspace 1. Configure workspace resources 2. Manage compute resources 3. Configure security and access - Prepare development environments 1. Configure environments 2. Use SDKs and notebooks