As of January 22, 2020, the DP 100 exam has been updated. See DP 100 Updated post. This content does not reflect the current exam.

I took and passed the exam during the beta period. These are my memories of the topics on the exam.

General Overview

Below is the basic structure of the DP-100: Designing and Implementing a Data Science Solution on Azure. Passing the exam will qualify you for the Azure Data Scientist Associate certification. It can be taken in a traditional exam center or at your home (but you will be watched via video camera).

• 180 minutes
• 60 questions (45 Q&A, 15 about Case Studies)

The exam can be broken down into 4 components: Machine Learning, Azure ML Studio, Azure Products, and Python. Below is a breakdown of the topics I remember from the exam.

Machine Learning

These are topics which would be covered in a traditional machine learning course. Here are some of the specific topics I remember.

• Evaluation of Linear Regression
• Evaluation of Classification
• Positive/negative skew
• Poisson regression
• Fisher’s exact test
• Pearson
• PCA
• Deep learning – high-level, what is is for
• Neural Networks (RNN vs CNN vs DCN vs GAN)

Azure ML Studio

Azure ML Studio is a major focus of the exam, so you need to be fluent in how to use it. Questions ranged from the basics of how to import data all the way to specifics about certain modules.

• Model Building
• Model Evalutation
• SMOTE
• MICE
• feature extraction
• missing data questions
• AutoML

Azure Products

There were a number of questions from this category. The question would present you a scenario problem and ask which products would be useful for solving the problem. The questions did not go very deep into any of the products, but you will need to know the purpose of these products.

• Azure Machine Learning Service
• Blob storage – specifically how to get data in/out
• Azure Notebooks
• Azure Cognitive Services (high level)
• Kubernetes
• HDInsight
• Data Science Virtual Machine

Python

Python was the language of choice for the exam, so focus on it.

• Scikit-learn
• Azure Machine Learning SDK for Python
• Hyperparameters

Not on the exam

The following topics were not covered on my exam. The exam questions are pulled from a pool of questions, so it is possible these topics may be cover on a different person’s exam. In any case, these are definitely not major portions of the exam.

• R
• Power BI
• Publishing Azure ML models