Exam Tips: Designing and Implementing a Data Science Solution on Azure (DP-100)

Introduction

• Tips for the Azure DP-100 exam
• DP-100 overview
• What you should know

1. Creating Machine Learning Models

• Create machine learning models: Domain overview
• Data exploration and analysis using Python
• What are machine learning models?
• Understanding the types of machine learning models
• Machine learning model training and evaluation

2. Using Visual Tools to Create Machine Learning Models with Azure Machine Learning

• ML model creation with visual tools: Domain overview
• Understanding automated machine learning
• Understanding Azure Machine Learning designer
• Train and evaluate models with Azure Machine Learning designer

3. Building and Operating Machine Learning Solutions with Azure Machine Learning

• ML solutions with Azure Machine Learning: Domain overview
• Introduction to the Azure Machine Learning SDK
• Working with data in Azure Machine Learning
• Training a machine learning model
• Machine learning at scale
• Understanding hyperparameters
• Understanding privacy and ethics in Azure Machine Learning
• Understanding model predictions
• Monitoring and security in Azure Machine Learning

4. Building and Operating Machine Learning Solutions with Azure Databricks

• ML solutions with Azure Databricks: Domain overview
• Introduction to Azure Databricks
• Preparing and working with data
• Training, managing, and deploying models in Azure Databricks
• Tracking experiments in Azure Databricks
• Tuning hyperparameters in Azure Databricks
• Distributed deep learning

Conclusion

• Additional resources for DP-100 preparation
• Tips for exam day