Microsoft Azure Data Scientist Associate

The Microsoft Azure Data Scientist Associate (DP-100) certification validates expertise in applying data science and machine learning techniques to implement and run ML workloads on Azure. Using Azure Machine Learning, data scientists learn to design and create training environments, train models, optimize hyperparameters, and deploy models into production at scale. This certification is essential for professionals building end-to-end ML pipelines on Azure.

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What is the Microsoft Azure Data Scientist Associate (DP-100) Certification?

Who Should Take This Course?

Data scientists building and deploying ML models on Azure
Machine learning engineers working with Azure ML workspaces
AI/ML practitioners migrating workloads to Azure cloud
Research scientists using Azure compute for large-scale experiments
Data engineers building ML pipelines with Azure ML components
Software developers integrating ML models into Azure applications

What You Will Learn in the DP-100 Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Design and Prepare a Machine Learning Solution

Set up Azure Machine Learning workspaces and compute resources.

Azure Machine Learning workspace — setup and configuration
Compute targets — compute instances, clusters, and attached compute
Azure ML Studio and SDK v2 fundamentals
Data assets — datasets, datastores, and data versioning
Environments and curated environments for reproducibility

Explore Data and Train Models

Prepare data, run experiments, and train ML models using Azure ML.

Exploratory data analysis and feature engineering
Automated Machine Learning (AutoML) for classification, regression, and forecasting
Training scripts — MLflow tracking and experiment logging
Azure ML pipelines for multi-step training workflows
Distributed training with GPU compute clusters

Optimize and Manage Models

Tune hyperparameters, explain models, and manage the ML lifecycle.

Hyperparameter tuning with Azure ML Sweep jobs
Model interpretability and explainability with SHAP and LIME
Responsible AI — fairness assessment and bias detection
Model registry — versioning, tagging, and lifecycle management
Model monitoring and data drift detection

Deploy and Consume Models

Deploy trained models as real-time and batch inference endpoints.

Real-time inference with Managed Online Endpoints
Batch scoring with Batch Endpoints and parallel job steps
Model deployment with MLflow and custom containers
A/B testing and canary deployments for model rollouts
Integrating deployed endpoints with downstream applications

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Python programming proficiency — pandas, NumPy, scikit-learn, and matplotlib
Familiarity with ML concepts — supervised/unsupervised learning, model evaluation
Azure Fundamentals (AZ-900) or basic Azure cloud knowledge
Experience with Jupyter notebooks and data analysis workflows
Basic understanding of statistics and probability
Azure Data Fundamentals (DP-900) recommended but not required

Exam Information

Everything you need to know about the DP-100 certification exam.

Exam Component	Details
Exam Name	Designing and Implementing a Data Science Solution on Azure
Exam Code	DP-100
Exam Type	Multiple Choice, Case Studies, Drag-and-Drop, Active Screen
Total Questions	40–60
Passing Score	700 (out of 1000)
Exam Duration	100 minutes
Language	English, Japanese, Chinese (Simplified), Korean, Spanish, German, French, Portuguese (Brazil)
Exam Provider	Microsoft / Pearson VUE
Exam Focus	Designing, training, optimizing, and deploying ML solutions on Azure Machine Learning
Exam Registration	Register at microsoft.com/en-us/learning or via Pearson VUE testing centers
Retake Policy	24 hours before first retake; 14 days before subsequent retakes; 5 attempts per year
Certification Validity	1 year — renewable via free online renewal assessment on Microsoft Learn