Book Image

Microsoft Power BI Data Analyst Certification Guide

By : Orrin Edenfield, Edward Corcoran
5 (1)
Book Image

Microsoft Power BI Data Analyst Certification Guide

5 (1)
By: Orrin Edenfield, Edward Corcoran

Overview of this book

Microsoft Power BI enables organizations to create a data-driven culture with business intelligence for all. This guide to achieving the Microsoft Power BI Data Analyst Associate certification will help you take control of your organization's data and pass the exam with confidence. From getting started with Power BI to connecting to data sources, including files, databases, cloud services, and SaaS providers, to using Power BI’s built-in tools to build data models and produce visualizations, this book will walk you through everything from setup to preparing for the certification exam. Throughout the chapters, you'll get detailed explanations and learn how to analyze your data, prepare it for consumption by business users, and maintain an enterprise environment in a secure and efficient way. By the end of this book, you'll be able to create and maintain robust reports and dashboards, enabling you to manage a data-driven enterprise, and be ready to take the PL-300 exam with confidence.
Table of Contents (25 chapters)
1
Part 1 – Preparing the Data
6
Part 2 – Modeling the Data
11
Part 3 – Visualizing the Data
15
Part 4 – Analyzing the Data
18
Part 5 – Deploying and Maintaining Deliverables
21
Part 6 – Practice Exams

Summary

In this chapter, we learned about the advanced data modeling features of Power BI.

We learned how we can protect sensitive data in our data model by using sensitivity labels, and how sensitivity labels integrate with a holistic data protection suite across Microsoft products and services. We learned that sensitivity labels can be configured to follow the data even when it is exported from Power BI into other data stores, such as Microsoft Excel.

We also learned how row-level security can be implemented and used. We learned that row-level security can be used to tailor the data in a report to the specific users or groups of users who are consuming the reports, so we don't need to make costly data model changes.

Lastly, we learned about Q&A and how it can be used to dynamically create report and dashboard visuals from natural-language queries written by report designers or report users. We learned that Q&A can be used to help users explore data made available...