DP-100 · ● Retiring (June 1, 2026) · Associate · Microsoft
Retirement notice: The DP-100 certification will retire on June 1, 2026 at 11:59 PM CST. Microsoft recommends transitioning to the AI-300 (Machine Learning Operations Engineer Associate) exam for continued growth in Azure AI and ML operations. Current holders can renew DP-100 via free online renewal assessment on Microsoft Learn before the retirement date.
Exam facts
| Field | Value |
|---|---|
| Cost | $165 USD (regional pricing may vary) |
| Duration | 180 minutes (exam); 210 minutes total scheduled (includes survey & instructions) |
| Questions | 40–60 mixed format (multiple-choice, multiple-response, case studies, hands-on labs) |
| Passing | 700 out of 1000 (scaled score) |
| Format | Mixed: multiple-choice, multiple-response, case studies, hands-on labs |
| Delivery | Pearson VUE (on-site) or OnVUE (remote proctored) |
| Languages | English (primary); additional languages available via request |
| Valid | 3 years from issue date |
| Renewal | Free online renewal assessment on Microsoft Learn before June 1, 2026 |
| Prerequisites | Familiarity with Python and ML frameworks (Scikit-Learn, PyTorch, TensorFlow); practical data science experience recommended |
| Released | 2019 (initial launch); current version: 2024 |
| Retiring | June 1, 2026, 11:59 PM CST |
Vendor source — Microsoft Azure Data Scientist Certification ↗ Official exam guide — DP-100 Study Guide ↗ Exam objectives — Official Exam Objectives ↗
About
The DP-100 certification validates the ability to design, build, and deploy end-to-end machine learning solutions on Azure. Launched in 2019, it targets intermediate-level data scientists with existing Python and ML framework expertise who need to leverage Azure Machine Learning (AML) for production workloads. The exam covers the complete ML lifecycle: data preparation, model experimentation, training optimization, deployment, and monitoring. As of June 1, 2026, Microsoft is retiring this certification in favor of the AI-300 (MLOps Engineer Associate), which shifts emphasis toward production deployment, CI/CD automation, and enterprise governance. Data scientists holding DP-100 can renew using the free online renewal assessment before the retirement date.
Domain context — Data Science & AI
Machine learning and AI engineering as applied to cloud platforms. Microsoft Azure ML provides managed infrastructure for end-to-end data science workflows, model training at scale, and production deployment via endpoints. Covers supervised and unsupervised learning, deep learning, automated ML, and responsible AI principles. Cross-listed with cloud, architecture, and DevOps contexts.
Read full deep dive — Microsoft Azure + M365 Ecosystem →
Topics covered
Exam objectives carry the following weights per Microsoft's official blueprint:
-
Design and prepare a machine learning solution (20–25%)
- Design ML solutions for business requirements
- Prepare data for model training
- Choose appropriate compute resources and datastores
-
Explore data and train models (35–40%)
- Explore and visualize data using Python
- Train supervised and unsupervised models
- Optimize hyperparameters and experiment with models
- Train deep learning models
-
Prepare model for deployment (20–25%)
- Evaluate model performance
- Prepare models for deployment
- Create and manage pipelines
-
Deploy and retrain a model (10–15%)
- Deploy models to endpoints
- Consume model endpoints (real-time and batch)
- Monitor model drift and retrain
Source: Official exam guide ↗
Common skills at Data Science & AI · Associate
Shared foundational capabilities expected for associate-level practitioners in ML/AI:
- Python programming (Pandas, NumPy, Scikit-Learn)
- Data exploration, cleaning, and feature engineering
- Model training, evaluation, and hyperparameter tuning
- Supervised learning (regression, classification)
- Unsupervised learning (clustering, dimensionality reduction)
- Performance metrics and cross-validation
- Introduction to deep learning and neural networks
- Jupyter notebooks and ML development workflows
- Version control and reproducible research practices
- Responsible AI and model interpretability basics
Recommended courses at Data Science & AI · Associate
| Provider | Title | Cost | URL |
|---|---|---|---|
| Microsoft Learn | Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure | Free | ↗ |
| LinkedIn Learning | Prepare for the Azure Data Scientist Associate (DP-100) Certification Learning Path | $30–$40/month | ↗ |
| Pluralsight | Microsoft Azure (DP-100): Designing and Implementing a Data Science Solution on Azure | $199–$399/year | ↗ |
| Coursera | No cert-specific course; general Azure ML specializations available | Free–$200 | Coursera Azure ↗ |
| Udemy | Exam DP-100: Microsoft Azure Data Scientist Practice Tests | $10–$15 | ↗ |
Practice exams
| Provider | Title | Cost | URL |
|---|---|---|---|
| MeasureUp | DP-100 Certification Practice Test | $99–$129 | ↗ |
| Whizlabs | Microsoft Azure DP-100 Certification Practice Exams | $49–$69 | ↗ |
| ExamTopics | Free DP-100 Exam Questions (community-sourced, unofficial) | Free | ↗ |
Practice exam note: MeasureUp offers 134 questions in certification and practice modes. Passing the MeasureUp exam twice in certification mode is considered a strong readiness indicator. Whizlabs and MeasureUp are commonly used together by candidates in their study routine.
Books
| Title | Author | Publisher | Year | ISBN | URL |
|---|---|---|---|---|---|
| Exam Ref DP-100: Designing and Implementing a Data Science Solution on Azure | Dayne Sorvisto | Microsoft Press | 2024 | 978-0135350607 | ↗ |
Book note: The current edition by Dayne Sorvisto (2024, Microsoft Press) covers Azure Machine Learning, Python, Jupyter notebooks, data ingestion, model training, deployment, and monitoring with MLflow. Earlier editions by Pierstefano Tucci are still available but reflect earlier exam versions. Recommend the Sorvisto edition for 2026 preparation.
Typical job titles at Data Science & AI · Associate
Data Scientist (Azure focus) · ML Engineer (Azure) · Cloud Data Scientist · Azure ML Engineer · Data Scientist II (Azure) · ML Operations Engineer (associate-level)
(Job titles drawn from current job-board postings that list DP-100, AI-102, or equivalent Azure ML experience as required or strongly preferred.)
Salary
| Region | Range | Source |
|---|---|---|
| USD | $133,500 – $243,000 (25th–90th percentile); average $160,000–$165,000 | ZipRecruiter ↗ · PayScale ↗ · Glassdoor ↗ |
| ZAR | No region-specific salary data available — use South African data scientist and cloud engineer postings as proxy; typical range R800,000–R1,500,000 annually | CareerJunction ↗ · Pnet ↗ |
| GBP | No region-specific data available — UK-based Azure data scientist roles typically £60,000–£110,000 | IT Jobs Watch ↗ |
Salary note: Published salary data reflects Azure Data Scientist roles requiring DP-100 or equivalent Azure ML experience. Compensation varies significantly by experience level (entry vs. mid vs. senior), employer size, and location. US data is most comprehensive; regional data for ZAR and GBP is limited and should be cross-checked with general cloud engineering and data science surveys.
Skills validated
Exam-specific technical capabilities distinct from the shared "Common skills" section:
- Azure Machine Learning workspace setup and configuration
- Data ingestion and preparation using Azure Data Factory and Synapse
- Feature engineering and transformation with Pandas/NumPy
- Model training using Azure ML training compute (compute instances, training clusters)
- Hyperparameter tuning and hyperparameter sweep experiments
- Automated Machine Learning (AutoML) for rapid prototyping
- Deep learning with PyTorch and TensorFlow on Azure compute
- Model evaluation, cross-validation, and performance metrics
- Pipeline creation and orchestration in Azure ML
- Model registration, versioning, and model registry
- Real-time and batch endpoint deployment
- Model monitoring, drift detection, and retraining triggers
- MLflow integration for experiment tracking
- Responsible AI: model interpretability (SHAP, permutation importance) and fairness assessment
- Security: authentication, RBAC, and data privacy in Azure ML
- Cost optimization for training and inference workloads
Related certifications
- Entry-level prerequisites: AI-900 (Azure AI Fundamentals) ↗ · DP-900 (Azure Data Fundamentals) ↗
- Parallel path at associate level: AI-102 (Azure AI Engineer Associate) ↗
- Recommended transition after retirement: AI-300 (Machine Learning Operations Engineer Associate) ↗ — Focuses on MLOps, deployment automation, CI/CD, and production governance
- Advanced / specialist roles: AZ-305 (Azure Solutions Architect Expert) ↗ · AZ-700 (Azure Network Engineer Associate) ↗
- Vendor overview: Microsoft Credentials & Certifications Overview ↗
Sources
- Microsoft Certified: Azure Data Scientist Associate ↗
- DP-100 Exam Skills Outline and Objectives ↗
- Microsoft Learn: Course DP-100T01-A ↗
- DP-100 Credential Renewal ↗
- Microsoft Certification Retirements ↗
- Vladtalkstech: Microsoft Certification Retirements 2026 ↗
- CBT Nuggets: DP-100 Retirement Analysis ↗
- MeasureUp DP-100 Practice Test ↗
- Whizlabs DP-100 Exam Preparation ↗
- Microsoft Press Store: Exam Ref DP-100 ↗
- ZipRecruiter: Azure Data Scientist Salary ↗
- PayScale: Data Scientist with Microsoft Azure ↗
- Glassdoor: Microsoft Data Scientist Salaries ↗
Last verified: 2026-05-01 Parent ecosystem: Microsoft Azure + M365 Ecosystem Parent domain: Data Science & AI Vendor overview: Microsoft Vendor Overview