Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

by Ginger Grant, Julio Granados, Guillermo Fernandez, Pau Sempere, Javier Torrenteras

Overview

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

About the Exam

Exam 70-774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data’s behavior; develop models and identify optimal algorithms; train, validate, deploy, manage, and consume Azure Machine Learning Models; and leverage related services and APIs.

About Microsoft Certification

Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certification, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services.

See full details at: microsoft.com/learning

Read Full Overview

Product Details

ISBN-13:	9781509307012
Publisher:	Pearson Education
Publication date:	02/23/2018
Series:	Exam Ref
Edition description:	New Edition
Pages:	336
Product dimensions:	7.30(w) x 8.90(h) x 0.90(d)

About the Author

Ginger Grant is a managing consultant at SolidQ, where she provides advanced analytic solutions and training. Julio Granados from Barranquilla, Colombia is Data Platform Engineer at SolidQ. Guillermo Fernández is Data Platform Specialist DPS at SolidQ, where he works every day on BI and data science projects. Pau Sempere is Mentor at SolidQ, participating in SQL Server, BI, Big Data and Data Science projects. Javier Torrenteras is Mentor at SolidQ, leading the business analytics department. Paco Gonzalez, based in Atlanta, is a Microsoft Data Platform MVP and Managing Director for SolidQ North America. Tamanaco Francisquez is a DPS with SolidQ.

Table of Contents

Chapter 1 Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning

Skill 1.1: Import and export data to and from Azure Machine Learning

Skill 1.2: Explore and summarize data

Skill 1.3: Cleanse data for Azure Machine Learning

Skill 1.4 Perform feature engineering

Chapter 2 Develop machine learning models

Skill 2.1: Select an appropriate algorithm or method

Skill 2.2: Initialize and train appropriate models

Skill 2.3: Validate models

Chapter 3 Operationalize and manage Azure Machine Learning Services

Skill 3.1: Deploy models using Azure Machine Learning

Skill 3.2: Manage Azure Machine Learning projects and workspaces

Skill 3.3: Consume Azure Machine Learning models

Skill 3.4: Consume exemplar Cognitive Services APIs

Chapter 4 Use other services for machine learning

Skill 4.1: Build and use neural networks with the Microsoft Cognitive Toolkit

Skill 4.2: Streamline development by using existing resources

Skill 4.3: Perform data science at scale by using HDInsight

Skill 4.4: Perform database analytics by using SQL Server R Services on Azure