DP-100 Data Science on Azure

• Explain the data science lifecycle and its role in business decision-making

• Understand how data science supports AI-driven business solutions

• Navigate the Microsoft Azure portal

• Identify Azure services used for data science and machine learning

• Organize and manage Azure resources for machine learning workflows

• Create and manage Azure Machine Learning workspaces

• Configure compute targets and environments

• Manage datasets and data assets within Azure ML

• Connect to various data sources including databases and cloud storage

• Perform Exploratory Data Analysis (EDA)

• Identify data quality issues and patterns

• Interpret data insights that influence model development decisions

• Clean datasets by handling missing values and outliers

• Apply feature selection and basic feature engineering concepts

• Use Azure Data Factory for data ingestion

• Use Azure Databricks for scalable data processing workflows

• Build repeatable data preparation workflows

• Differentiate regression, classification, and clustering problems

• Follow the machine learning lifecycle

• Identify common algorithms and appropriate use cases

• Map machine learning workflows to Azure Machine Learning services

• Use Azure Machine Learning Studio for model development

• Create experiments and pipelines

• Split data for training, validation, and testing

• Train models using prepared datasets

• Interpret model outputs, predictions, and evaluation metrics

• Apply regression and classification evaluation metrics

• Compare performance across multiple machine learning models

• Tune hyperparameters to improve accuracy

• Track experiments and results within Azure ML

• Understand model deployment concepts

• Deploy models as web services

• Manage endpoints and inference workflows

• Apply scalable deployment practices

• Implement model versioning and deployment best practices

• Monitor deployed models

• Plan model retraining workflows

• Ensure scalability, reliability, and security

• Apply governance, monitoring, and MLOps concepts in Azure environments

• Understand Azure Cognitive Services and use cases

• Apply prebuilt AI services for vision, language, and speech

• Combine cognitive services with custom machine learning solutions

• Evaluate real-world AI integration scenarios

• Analyze DP-100 style architecture scenarios

• Evaluate Azure ML solution design decisions

• Review enterprise deployment and governance considerations

• Apply Azure best practices across machine learning workflows

Apply Microsoft Azure services to design, build, deploy, and present a complete end-to-end machine learning solution for a structured real-world business scenario.