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Microsoft Azure Data Scientist Associate (DP-100)

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!