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.
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
Fisher’s exact test
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.
missing data questions
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 Cognitive Services (high level)
Data Science Virtual Machine
Python was the language of choice for the exam, so focus on it.
Azure Machine Learning SDK for Python
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.
Microsoft Azure has an abundance of data science capabilities (and non-data science capabilities). It can be challenging to keep up with the latest updates/releases. Luckily, Azure has a page to let you know exactly what has changed. You just need to know where to find it, and the following video will help you find that page.
Read to the end to learn more about a new study group I will be launching.
In Late January 2019, Microsoft launched 3 new certifications aimed at Data Scientists/Engineers. For a while, Microsoft has been toying with different methods for training and credentials. They launched the Microsoft Professional Program in Data Science back in 2017. While it provides great content, it did not result in either a college diploma or an official Microsoft certification. Now Microsoft is in the process of restructing certifications to be more role-focused. Here are details about the 3 certification of interest to data scientists and data engineers.
Here are the latest articles from Microsoft regarding cloud data science products and updates. This week it includes Measuring Model Goodness, a free ebook, AI discovery days, and more AI goodness.
Measuring Model Goodness – Part 2 – Measurability is an important aspect of the Team Data Science Process (TDSP) as it quantifies how good the machine learning model is for the business and helps gain acceptance from the key stakeholders. In part 1 of this series, we defined a template for …[Read More]
AI Discovery Days at a Microsoft Location Near You – Join us to learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning.[Read More]
Describe, diagnose, and predict with IoT Analytics – … in IoT Analytics Machine learning (ML) is playing an increasingly important role in IoT analytics. One could argue that the recent emergence of real-world applications of ML in manufacturing is thanks to the explosion of data, most of which we can …[Read More]
Here are the latest articles from Microsoft regarding cloud data science products and updates. Find the latest on AI, Azure Functions, IoT and more.
Bot Framework v4 with Luis – The Cognitive Services Language Understanding Intelligence Services (Luis … Luis uses a thing called a ‘Resolution’ to provide additional data with these kinds of complex entities so that you can resolve the actual values from the words the user said.[Read More]