We’re happy to announce the release of Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning (ISBN 9781509307012), by Ginger Grant, Julio Granados, Guillermo Fernández, Pau Sempere, Javier Torrenteras, Paco González, and Tamanaco Francísquez.
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Prepare for Microsoft Exam 70-774—and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.
Focus on the expertise measured by these objectives:
• Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning
• Develop machine learning models
• Operationalize and manage Azure Machine Learning Services
• Use other services for machine learning
This Microsoft Exam Ref:
• Organizes its coverage by exam objectives
• Features strategic, what-if scenarios to challenge you
• Assumes you are familiar with Azure data services, machine learning concepts, and common data science processes
The 70-774 exam focuses on the features and functionalities available to properly perform data science activities using Azure Machine Learning. It covers the tasks needed to properly prepare data to be analyzed in Azure Machine Learning and how to understand and find the key variables that are describing data behavior. This book also covers how to to develop Machine Learning Models, identifying the best suited algorithm, and describing the train and validation steps that need to be taken to obtain the best in class models. The book also covers how to deploy, manage, and consume Azure Machine Learning Models, and how to leverage other services.
This book is focused on data scientists or analysts who are interested in learning about the use of Azure cloud services to build and deploy Azure Machine Learning Models. We also cover how to build intelligence systems that can complement other services like Cognitive Services APIS, templates from the Cortana intelligence Gallery, HDInsight, or R Server services.
This book covers every major topic area found on the exam, but it does not cover every exam question. Only the Microsoft exam team has access to the exam questions, and Microsoft regularly adds new questions to the exam, making it impossible to cover specific questions. You should consider this book a supplement to your relevant real-world experience and other study materials. If you encounter a topic in this book that you do not feel completely comfortable with, use the “Need more review?” links you’ll find in the text to find more information and take the time to research and study the topic. Great information is available on MSDN, TechNet, and in blogs and forums.
Organization of this book
This book is organized by the “Skills measured” list published for the exam. The “Skills measured” list is available for each exam on the Microsoft Learning website: https://aka.ms/examlist. Each chapter in this book corresponds to a major topic area in the list, and the technical tasks in each topic area determine a chapter’s organization. If an exam covers six major topic areas, for example, the book will contain six chapters.
Preparing for the exam
Microsoft certification exams are a great way to build your resume and let the world know about your level of expertise. Certification exams validate your on-the-job experience and product knowledge. Although there is no substitute for on-the-job experience, preparation through study and hands-on practice can help you prepare for the exam. We recommend that you augment your exam preparation plan by using a combination of available study materials and courses. For example, you might use the Exam ref and another study guide for your ”at home” preparation, and take a Microsoft Official Curriculum course for the classroom experience. Choose the combination that you think works best for you.
Note that this Exam Ref is based on publicly available information about the exam and the author’s experience. To safeguard the integrity of the exam, authors do not have access to the live exam.
About the authors
GINGER GRANT is a managing consultant at SolidQ, where she provides advanced analytic solutions and training. Ginger started working in data science to provide machine learning solutions across a wide range of industries including insurance, education, healthcare, finance and transportation. She was able to leverage expertise gained by developing business intelligence projects, where she applied the knowledge gained through her MCSA certification in SQL Server. These efforts encouraged her to pursue the greater analytic capabilities in data science and Azure Machine Learning. She is a prolific blogger at http://www.desertislesql.com and frequent speaker at data conferences and events worldwide on topics such as R, Power BI, Python and Azure Machine Learning to introduce more people to current developments and future trends in data science and machine learning. Microsoft has recognized her technical contributions by awarding her a MVP in Data Platform. You can follow her on Twitter at http://twitter.com/desertislesql.
JULIO GRANADOS from Barranquilla, Colombia is DPE, a data platform engineer at SolidQ. He has been a collaborating professor at EAE Business School in Barcelona and speaker at SolidQ Summit. He completed his degree in Computer science specialized in information systems at the University of Alicante, Spain and a Master in Business Intelligence in Microsoft Technologies taught by SolidQ You can follow him on linkedIn: https://www.linkedin.com/in/julio-granados-barros-398ba4a4/.
GUILLERMO FERNÁNDEZ Vizcaíno is Data Platform Specialist at SolidQ, where he works every day on BI and data science projects. Guillermo is a graduate of University of Alicante with a degree in computer engineering. Since his university days, he has been focused on the study of AI algorithms, especially machine learning and deep learning. He recently completed a master's degree in Business Intelligence with Microsoft tools at SolidQ.
PAU SEMPERE SÁNCHEZ is Mentor at SolidQ, participating in SQL Server, BI, Big Data and Data Science projects. Pau is a computer engineer graduated at University of Alicante, being awarded as Microsoft Active Professional in 2012. He started working with SQL Server relational databases and switched to OLAP analytical models, data mining models, cloud technologies, big data and AI systems. He participates regularly as speaker at Microsoft events, SolidQ Summits and Data Platform community events. He is member of the PASS Spanish group, being founder and organizer of SQL Saturday Spain.
JAVIER TORRENTERAS is Mentor at SolidQ, leading the business analytics department. Javier is a computer engineer graduated at University Politécnica de Madrid, as long as having a Masters degree in education and e-learning by the University of Alcala. He has been working in the IT business since 1996, mainly involved in data analytics projects. As a specialized BI professional he has also been involved as a speaker in public events and also as a teacher on some business schools and universities.
PACO GONZALEZ, based in Atlanta, is a Microsoft Data Platform MVP and Managing Director for SolidQ North America. He is a speaker at relevant conferences such as: Ignite, PASS BA, PASS Summit, DevWeek London, PAW Chicago and London. Paco holds a BS in Computer engineering and a Master's degree in Artificial Intelligence from University of Murcia, Spain.
TAMANACO FRANCISQUEZ is a DPS with SolidQ. A physicist by formal training, he has a Master's Degree in Molecular Nanotechnology and during his academic research worked extensively with Matlab. Having switched his interest and focus into R, R Server, machine learning and citizen data science, he has extensive experience with analytical models, statistical methods and machine learning. He is currently finishing a Master's Degree in Business Intelligence and is pursuing his first MCSA Certification.