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Microsoft Certified Azure Data Fundamentals (DP-900) Exam Guide

Microsoft Certified Azure Data Fundamentals (DP-900) Exam Guide

By : Steve Miles
5 (2)
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Microsoft Certified Azure Data Fundamentals (DP-900) Exam Guide

Microsoft Certified Azure Data Fundamentals (DP-900) Exam Guide

5 (2)
By: Steve Miles

Overview of this book

Microsoft's Azure Data Fundamentals (DP-900) certification exam validates your expertise in core data concepts and Azure’s powerful data services capabilities. This comprehensive guide written by Steve Miles—a Microsoft Azure MVP and certified trainer with over 25 years of experience in cloud data services and 30+ certifications across major platforms—serves as your gateway to a future shaped by data and AI, regardless of your technical background. With the help of examples, you'll learn fundamental data concepts, including data representation, data storage options, and common workloads and gain clarity on the roles and responsibilities of key data professionals such as data administrators, engineers, and analysts. This guide covers all crucial exam domains, from data services capabilities of the Azure cloud platform to considerations for relational, non-relational, and analytics workloads, encompassing both Microsoft and open-source technologies. To supplement your exam prep, this book gives you access to a suite of online resources designed to boost your confidence, including mock tests, interactive flashcards, and invaluable exam tips By the end of this book, you’ll be fully prepared not only to pass the DP-900 exam but also to confidently tackle data solutions in Azure, setting a strong foundation for your data-driven career
Table of Contents (11 chapters)
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Describe Core Concepts of Data Modeling

Analytical models give you a structure for the data you want to analyze. In the following sections, we will explore some core concepts, such as cubes and schemas.

Cubes

Analytical models are based on related data tables that contain the following properties:

  • Measures: These are the numeric values for a model; they can include sales metrics such as quantity or revenue
  • Dimensions: These are descriptive attributes for a specific aspect of data, such as customer, product, and time

A typical data table in a business context often contains quantitative sales metrics (measures), such as revenue or quantity, and categorical dimensions, such as product, customer, and time. With this data, analysts can perform in-depth analyses to derive insights, such as the total revenue by an individual customer or the monthly number of items sold by a product.

Conceptually, a model becomes a multidimensional cube, and at any point at which...

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