Book Image

Architecting Microsoft Azure Solutions - Exam Guide 70-535

By : Sjoukje Zaal
Book Image

Architecting Microsoft Azure Solutions - Exam Guide 70-535

By: Sjoukje Zaal

Overview of this book

Architecting Microsoft Azure Solutions: Exam Guide 70-535 will get Azure architects and developers up-to-date with the latest updates on Azure from an architecture and design perspective. The book includes all the topics that are still relevant from the previous 70-534 exam, and is updated with latest topics covered, including Artificial Intelligence, IoT, and architecture styles. This exam guide is divided into six parts, where the first part will give you a good understanding of how to design a compute infrastructure. It also dives into designing networking and data implementations. You will learn about designing solutions for Platform Service and operations. Next, you will be able to secure your resources and data, as well as design a mechanism for governance and policies. You will also understand the objective of designing solutions for Platform Services, by covering Artificial Intelligence, IoT, media services, and messaging solution concepts. Finally, you will cover the designing for operations objective. This objective covers application and platform monitoring, as well as designing alerting strategies and operations automation strategies. By the end of the book, you’ll have met all of the exam objectives, and will have all the information you need to ace the 70-535 exam. You will also have become an expert in designing solutions on Microsoft Azure.
Table of Contents (20 chapters)
Appendix A – Assessments
Appendix B – Mock Test Questions
Appendix C – Mock Test Answers

Azure Machine Learning

We all produce massive amounts of data these days. Data created in the past can give us valuable insights into the future.

Machine learning provides a set of algorithms that can apply complex mathematical calculations to big data automatically and can eventually learn from this data. It is a data science technique, which can be used to predict the future by forecasting outcomes and trends. Applications using this technique can learn from data and experiences without being explicitly programmed. Machine learning models can be trained using large amounts of historical data which applications can act upon.

Fraud detection, self-driving vehicles, and personal recommendations on websites are all examples of applications that use machine learning. Artificial intelligence and machine learning possibilities are endless and will have an enormous impact on our daily...