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Microsoft Azure Data Scientist Associate (DP-100) - RETIRED

Important Notice

The DP-100 exam and its certification were retired on June 1, 2026. It can no longer be taken and the certification can no longer be earned or renewed. Credentials already earned stay on the holder's Microsoft Learn transcript. This guide is kept as historical reference.

Source: Microsoft Learn - exam retirement

Exam Overview

The Azure Data Scientist Associate certification validates your knowledge of applying data science and machine learning to implement and run machine learning workloads on Azure.

Exam Details: - Exam Code: DP-100 - Duration: 180 minutes - Number of Questions: 40-60 questions - Passing Score: 700 out of 1000 - Question Types: Multiple choice, multiple select, drag and drop, case studies - Cost: $165 USD - Prerequisites: Data science and ML experience with Azure

Exam Domains

1. Design and Prepare a Machine Learning Solution (20-25%)

  • Solution Planning
  • Define business objectives
  • Choose appropriate ML techniques
  • Plan data requirements

  • Design compute infrastructure

  • Azure ML Workspace

  • Create and configure workspaces
  • Manage compute resources
  • Configure datastores
  • Implement security and compliance

2. Explore Data and Train Models (35-40%)

  • Data Exploration
  • Explore data with notebooks
  • Perform feature engineering
  • Handle missing and imbalanced data

  • Prepare data for modeling

  • Model Training

  • Train models with Azure ML
  • Use automated ML (AutoML)
  • Implement hyperparameter tuning
  • Implement custom training scripts
  • Monitor training runs

3. Prepare Models for Deployment (20-25%)

  • Model Evaluation
  • Evaluate model performance
  • Interpret model results
  • Compare model metrics

  • Implement responsible AI practices

  • Model Registration

  • Register models in Azure ML
  • Manage model versions
  • Package models for deployment
  • Create model profiles

4. Deploy and Retrain Models (10-15%)

  • Model Deployment
  • Deploy to Azure Kubernetes Service (AKS)
  • Deploy to Azure Container Instances (ACI)
  • Deploy to Azure ML endpoints

  • Implement batch inference pipelines

  • Model Monitoring

  • Monitor model performance
  • Implement data drift detection
  • Configure Application Insights
  • Retrain models on new data

Key Azure ML Components

Development

  • Notebooks - Interactive development with Jupyter
  • Automated ML - No-code ML training
  • Designer - Drag-and-drop ML pipelines
  • VS Code Integration - Local development

Training

  • Compute Instances - Development environments
  • Compute Clusters - Scalable training
  • Experiments - Training run tracking
  • Pipelines - ML workflow automation

Deployment

  • Real-time Endpoints - Online inference
  • Batch Endpoints - Batch predictions
  • Azure Kubernetes Service - Production deployment
  • Edge Deployment - IoT scenarios

MLOps

  • Model Registry - Version management
  • Environments - Dependency management
  • Pipelines - CI/CD automation
  • Monitoring - Model performance tracking

Study Tips

  • Weeks 1-2: Azure ML fundamentals and workspace setup
  • Weeks 3-4: Data exploration and preparation
  • Weeks 5-6: Model training (AutoML and custom)
  • Weeks 7-8: Model evaluation and interpretation
  • Weeks 9-10: Deployment and monitoring
  • Weeks 11-12: MLOps and practice exams

Key Study Resources

  • Microsoft Learn: DP-100 learning path (FREE)
  • Azure ML Documentation: Technical reference
  • Sample Notebooks: Azure ML examples on GitHub
  • Hands-on Labs: Build end-to-end ML solutions

Hands-on Practice Requirements

  • Create and manage Azure ML workspaces
  • Prepare data for machine learning
  • Train models using various methods (AutoML, custom scripts)
  • Implement hyperparameter tuning
  • Deploy models to various targets
  • Monitor model performance and data drift
  • Build ML pipelines

Python Libraries to Master

  • Azure ML SDK - Python SDK for Azure ML
  • scikit-learn - ML algorithms
  • pandas - Data manipulation
  • matplotlib/seaborn - Visualization
  • numpy - Numerical computing

📚 Comprehensive Study Resources

👉 Complete Azure Study Resources Guide

Prerequisites and Next Steps

Prerequisites

  • Experience with Python programming
  • Understanding of ML concepts and algorithms
  • Knowledge of data science fundamentals
  • Familiarity with Jupyter notebooks
  • Basic Azure knowledge helpful

Career Path

  • Role Focus: Data Scientist, ML Engineer, AI Engineer
  • Skills Development: MLOps, responsible AI, model optimization
  • Next Steps: Consider AI-102 (AI Engineer) or specialized ML certifications

Remember: DP-100 requires practical ML experience with Azure ML. Hands-on model training and deployment is essential!