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  • Book Overview & Buying Microsoft Power BI Data Analyst Certification Guide
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Microsoft Power BI Data Analyst Certification Guide

Microsoft Power BI Data Analyst Certification Guide

By : Edenfield, Corcoran
3.9 (26)
Buy this Book
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Microsoft Power BI Data Analyst Certification Guide

Microsoft Power BI Data Analyst Certification Guide

3.9 (26)
By: Edenfield, Corcoran
Buy this Book

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)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Download the color images
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Conventions used
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Get in touch
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Share Your Thoughts
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Learn more on Discord
1
Part 1 – Preparing the Data
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Part 1 – Preparing the Data
Lock Free Chapter
2
Chapter 1: Overview of Power BI and the PL-300 Exam
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Chapter 1: Overview of Power BI and the PL-300 Exam
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A brief overview of Power BI
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Why get certified?
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PL-300 Analyzing Data with Microsoft Power BI
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Summary
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Questions
3
Chapter 2: Connecting to Data Sources
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Chapter 2: Connecting to Data Sources
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Technical requirements
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Identifying data sources
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Connecting to data sources
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Power BI datasets
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Power BI dataflows
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Query performance tuning
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Advanced options (what-if parameters, Power Query parameters, PBIDS files, and XMLA endpoints)
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Summary
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Questions
4
Chapter 3: Profiling the Data
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Chapter 3: Profiling the Data
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Technical requirements
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Identifying data anomalies
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Interrogating data statistics
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Summary
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Questions
5
Chapter 4: Cleansing, Transforming, and Shaping Data
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Chapter 4: Cleansing, Transforming, and Shaping Data
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Technical requirements
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Accessing Power Query in Power BI
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Sorting and filtering
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Managing columns
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Using column transformations
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Using row transformations
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Combining data
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Enriching data with AI
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Using advanced operations of Power Query
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Summary
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Questions
6
Part 2 – Modeling the Data
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Part 2 – Modeling the Data
7
Chapter 5: Designing a Data Model
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Chapter 5: Designing a Data Model
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Technical requirements
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Define the tables
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Flatten out a parent-child hierarchy
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Define role-playing dimensions
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Configure table and column properties
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Define quick measures
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Resolve many-to-many relationships
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Create a common date table
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Define the appropriate level of data granularity
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Design the data model to meet performance requirements
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Summary
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Questions
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Chapter 6: Using Data Model Advanced Features
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Chapter 6: Using Data Model Advanced Features
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Technical requirements
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Using sensitivity labels
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Implementing row-level security
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Applying natural-language Q&A capability
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Summary
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Questions
9
Chapter 7: Creating Measures Using DAX
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Chapter 7: Creating Measures Using DAX
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Technical requirements
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Building complex measures with DAX
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Using CALCULATE to manipulate filters
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Implementing time intelligence using DAX
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Replacing numeric calculated columns with measures
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Using basic statistical functions to enhance data
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Implementing top N analysis
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Creating semi-additive measures
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Summary
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Questions
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Chapter 8: Optimizing Model Performance
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Chapter 8: Optimizing Model Performance
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Technical requirements
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Optimizing data in the model
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Optimizing measures, relationships, and visuals
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Optimizing with aggregations
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Query diagnostics
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Summary
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Questions
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Part 3 – Visualizing the Data
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Part 3 – Visualizing the Data
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Chapter 9: Creating Reports
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Chapter 9: Creating Reports
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Technical requirements
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Understanding the capabilities of Power BI
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Adding visualization items to reports
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Choosing an appropriate visualization type
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Formatting and configuring visualizations
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Importing a custom visual
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Configuring conditional formatting
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Configuring small multiples
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Applying slicing and filtering
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Adding an R or Python visual
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Adding a smart narrative visual
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Configuring the report page
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Designing and configuring for accessibility
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Configuring automatic page refresh
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Creating a paginated report
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Using Power BI datasets in Excel PivotTables
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Summary
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Questions
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Chapter 10: Creating Dashboards
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Chapter 10: Creating Dashboards
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Technical requirements
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Introducing Power BI dashboards
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Pinning tiles
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Optimizing dashboards
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Summary
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Questions
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Chapter 11: Enhancing Reports
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Chapter 11: Enhancing Reports
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Technical requirements
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Using bookmarks
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Designing reports for mobile devices
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Summary
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Questions
15
Part 4 – Analyzing the Data
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Part 4 – Analyzing the Data
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Chapter 12: Exposing Insights from Data
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Chapter 12: Exposing Insights from Data
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Technical requirements
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Exploring slicers and filters
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The Analytics pane
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Summary
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Questions
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Chapter 13: Performing Advanced Analysis
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Chapter 13: Performing Advanced Analysis
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Technical requirements
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Identifying outliers
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Using anomaly detection
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Conducting time series analysis
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Grouping and binning
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Key influencers
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Decomposition tree visual
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Applying AI insights
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Summary
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Questions
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Part 5 – Deploying and Maintaining Deliverables
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Part 5 – Deploying and Maintaining Deliverables
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Chapter 14: Managing Workspaces
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Chapter 14: Managing Workspaces
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Technical requirements
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Using workspaces
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Distributing reports and dashboards
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Using deployment pipelines
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Monitoring workspace usage
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Summary
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Questions
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Chapter 15: Managing Datasets
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Chapter 15: Managing Datasets
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Technical requirements
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Configuring a dataset scheduled refresh
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Identifying when a gateway is required
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Configuring row-level security group membership
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Providing access to datasets
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Summary
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Questions
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Part 6 – Practice Exams
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Part 6 – Practice Exams
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Chapter 16: Practice Exams
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Chapter 16: Practice Exams
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Practice test 1
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Practice Test 2
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Answer keys
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Other Books You May Enjoy
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Other Books You May Enjoy
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Packt is searching for authors like you
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Share Your Thoughts
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Appendix: Practice Question Answers
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Appendix: Practice Question Answers
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Chapter 1, Overview of Power BI and the PL-300 Exam
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Chapter 2, Connecting to Data Sources
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Chapter 3, Profiling the Data
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Chapter 4, Cleansing, Transforming, and Shaping Data
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Chapter 5, Designing a Data Model
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Chapter 6, Using Data Model Advanced Features
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Chapter 7, Creating Measures Using DAX
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Chapter 8, Optimizing Model Performance
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Chapter 9, Creating Reports
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Chapter 10, Creating Dashboards
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Chapter 11, Enhancing Reports
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Chapter 12, Exposing Insights from Data
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Chapter 13, Performing Advanced Analytics
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Chapter 14, Managing Workspaces
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Chapter 15, Managing Datasets
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Using sensitivity labels

As part of the holistic data protection capabilities, Power BI includes integration with Microsoft Information Protection (MIP). MIP provides sensitivity labels as a way of making it easy for users to classify critical content and data while not making this a burden for the user or restricting collaboration.

Sensitivity labels allow users to classify datasets, reports, dashboards, and dataflows as having a specific sensitivity so that data can be protected. For example, some data used in Power BI reports might be publicly accessible data, and this feature allows those datasets to be labeled as such. In the same way, internal sales data might need to be protected and labeled as confidential; when this happens, the data is protected by the service from being accessed by those who do not have permission to access data labeled confidential. It can also enforce encryption, restrict forwarding, and, in some cases, printing as well.

In Power BI, sensitivity...

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Microsoft Power BI Data Analyst Certification Guide
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