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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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Summary

This chapter included complete coverage of the DP-900 Azure Data Fundamentals exam’s Skills Measured area – Describe the Common Elements of Large-scale Analytics.

You started this chapter by exploring considerations for data ingestion and processing, and understanding how to select appropriate technologies based on data variety, volume, and processing needs. This foundational knowledge is crucial, as effective data ingestion ensures that the right data is captured and made available for analysis promptly.

Then, the chapter delved into options for analytical data stores, discussing different architectures such as data warehouses, data lakes, and data lakehouses. Learning about these options is essential for understanding how to store and manage data in a way that supports diverse analytical workloads and enables actionable insights.

Finally, you explored the various Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks...

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