In this guide, I will explain the idea of Azure Data Scientist Associate, the content of the exam, preparations, and some keys take away after the course.

What is the Microsoft Azure DP-100 exam?

Before getting to the details, I would like to recap about the Microsoft Azure DP-100. _“Designing and Implementing a Data Science Solution on Azure” (or DP-100) _is the exam you need to take to get the Azure Data Scientist Associate. Based on Microsoft, this course tests your knowledge of data science and machine learning to implement and run machine learning workloads on Azure. There are 4 skills measured in the exam

• Setup an Azure Machine Learning workplace (30–35%).
• Run experiment and train models (25–30%).
• Optimize and manage models (20–25%).
• Deploy and consume models (20–25%).

More details, the exam has 55 questions (10 of them cannot be reviewed). The questions are categorized into 5 different types

• Multiple choice single answer
• Multiple choice multiple answer
• Arrange in the correct order
• Scenarios based questions
• Complete the code by fill in the blank

Oh, I almost forgot to mention that the exam took 180 minutes and able to take at home.

How do I prepare for it?

All of my learning resources are free from Microsoft. When I first sign up for the free account, Microsoft gives 200$ credits for 30 days. That enough to practice for the exam, actually I spent hardly around 50$ on it.

Since I have little knowledge about Azure, I’m decided to follow the DP-100 lab (https://github.com/MicrosoftLearning/DP100/tree/master/labdocs) provided by Microsoft on GitHub. It took me around 20 hours to finish the lab. The lab is divided into 10 modules but I can say it covers two major tools

• Azure Machine Learning Designer, which is a drag and drop tool that you can use to build, test, and deploy the predictive analytics solution.

• Using Azure Machine Learning SDK for Python to build, run, and deploy machine learning workflows with Azure Machine Learning services. You can interact with the service in any Python environment with the SDK.

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