ExamFinch.com My Study

Study guide

1.1 Data Source Types and Connections

Practice this exam

Microsoft Power BI Data Analyst PL-300

Track your progress as you read

Finished sections get a check mark.

1 Introduction

About the PL-300 Exam The Power BI Ecosystem

2 Chapter 1: Get or Connect to Data

1.1 Data Source Types and Connections 1.2 Storage Modes: Import, DirectQuery, and Direct Lake 1.3 Data Source Settings and Parameters

3 Chapter 2: Profile and Clean the Data

2.1 Data Profiling in Power Query 2.2 Resolving Errors, Nulls, and Data Quality Issues 2.3 Data Types and Type Detection

4 Chapter 3: Transform and Load the Data

3.1 Column Transformations and Data Types 3.2 Grouping, Aggregating, Pivoting, and Unpivoting 3.3 Merge Queries, Append Queries, and Relationship Keys 3.4 Query Folding and Load Performance

5 Chapter 4: Design and Implement a Data Model

4.1 Star Schema Design 4.2 Relationships, Cardinality, and Cross-Filter Direction 4.3 Table and Column Properties 4.4 Calculated Columns and Calculated Tables

6 Chapter 5: Create Model Calculations with DAX

5.1 DAX Fundamentals and Measures 5.2 The CALCULATE Function 5.3 Time Intelligence Measures 5.4 Statistical Functions, Semi-Additive Measures, and Calculation Groups 5.5 Essential DAX Patterns and Functions Reference

7 Chapter 6: Optimize Model Performance

6.1 Performance Analyzer and DAX Query View 6.2 Model Size Reduction and Cardinality Management 6.3 DAX Optimization Patterns

8 Chapter 7: Create Reports

7.1 Visual Types and Selection 7.2 Visual Formatting and Conditional Formatting 7.3 Copilot in Power BI Reports 7.4 Report Page Configuration and Slicers

9 Chapter 8: Enhance Reports for Usability and Storytelling

8.1 Bookmarks, Navigation, and Drillthrough 8.2 Sorting, Selection Pane, Mobile Layout, and Accessibility 8.3 Export Settings and Automatic Page Refresh

10 Chapter 9: Identify Patterns and Trends

9.1 The Analyze Feature and AI Visuals 9.2 Using Copilot to Summarize the Semantic Model 9.3 Advanced Analytics Patterns

11 Chapter 10: Create and Manage Workspaces and Assets

10.1 Workspaces, Apps, and Content Distribution 10.2 Subscriptions, Alerts, and Semantic Model Refresh 10.3 Dashboards and Advanced Distribution

12 Chapter 11: Secure and Govern Power BI Items

11.1 Row-Level Security (RLS) 11.2 Sensitivity Labels, Item Access, and Governance 11.3 Deployment Pipelines and Content Lifecycle

1.1 Data Source Types and Connections

Key Takeaways Explain This Topic AI

Power BI supports 100+ data connectors organized into categories: File, Database, Power Platform, Azure, Online Services, and Other.

Common file sources include Excel, CSV, JSON, XML, and Parquet files.

Database connectors support SQL Server, Azure SQL, PostgreSQL, MySQL, Oracle, and more.

Power Platform connectors include Dataverse, Dataflows, and Power BI datasets (semantic models).

Each connector has different capabilities for query folding, DirectQuery support, and authentication methods.

Last updated: June 2026

Quick Answer: Power BI connects to 100+ data sources through built-in connectors. The most common are Excel files, SQL Server databases, Azure services, SharePoint, and Power Platform sources like Dataverse. Each connector determines what storage modes are available and whether query folding is supported.

Connecting to data is the foundation of every Power BI project. The PL-300 expects you to know which connectors to use, how to configure them, and the implications of each connection type.

Connector Categories

Power BI organizes its 100+ connectors into these categories:

  • Category Examples Common Use Cases
  • File Excel, CSV/TSV, JSON, XML, Parquet, PDF Flat files, exports, local data
  • Database SQL Server, Azure SQL, PostgreSQL, MySQL, Oracle, Snowflake Transactional and analytical databases
  • Power Platform Dataverse, Dataflows, Power BI semantic models Microsoft ecosystem integration
  • Azure Azure SQL, Azure Synapse, Azure Data Lake Storage, Azure Blob Storage Cloud data warehouses and lakes
  • Online Services SharePoint Online, Dynamics 365, Salesforce, Google Analytics SaaS application data
  • Other Web, OData, ODBC, R/Python scripts, Blank query Custom and API-based sources
  • Connecting to File Sources
  • Excel Files

Excel is the most common entry point for Power BI data: Home tab → Get Data → Excel Workbook → Select file → Choose tables/sheets

Key considerations:

  • Power BI imports from named tables or worksheet ranges
  • Data should be in a tabular format with headers in row 1
  • Merged cells, subtotals, and empty rows cause import issues
  • Excel files stored on SharePoint or OneDrive can be set up for automatic refresh
  • CSV and Text Files

CSV files are loaded using the Text/CSV connector:

  • Power BI auto-detects delimiters (comma, tab, semicolon)
  • Encoding is detected automatically but can be overridden
  • First row is used as column headers by default
  • Large CSV files (100 MB+) may benefit from conversion to Parquet
  • JSON Files

JSON data is semi-structured and requires transformation:

  • Power BI parses JSON into tables using the JSON connector
  • Nested objects appear as Record types that must be expanded
  • Arrays appear as List types that must be expanded
  • The Json.Document() function in M handles parsing
  • Parquet Files

Parquet is a columnar storage format increasingly common in modern data platforms:

  • Highly compressed and optimized for analytical queries
  • Native connector in Power BI Desktop
  • Key format for Direct Lake mode with Microsoft Fabric
  • Supports complex data types including nested structures
  • Connecting to Databases
  • SQL Server

SQL Server is the most commonly tested database connector: Get Data → SQL Server → Enter server name → Optional: database name

Connection options:

  • Import mode : Data is copied into the Power BI model (default)
  • DirectQuery : Queries are sent to the server at report time
  • Advanced options : Custom SQL statement, command timeout, relationship detection

Authentication methods:

  • Windows authentication (Kerberos/NTLM)
  • Database authentication (SQL login)
  • Microsoft account
  • Azure Active Directory
  • Azure SQL Database

Similar to SQL Server but cloud-hosted:

  • Requires the full server name: servername.database.windows.net
  • Supports Azure AD authentication with MFA
  • Firewall rules must allow the connection
  • Supports both Import and DirectQuery
  • Azure Synapse Analytics

For large-scale analytical workloads:

  • Dedicated SQL pools and serverless SQL pools
  • Optimized for massive parallel processing (MPP)
  • DirectQuery is recommended for large datasets
  • Supports the same authentication methods as Azure SQL
  • Connecting to Power Platform Sources
  • Microsoft Dataverse

Dataverse (formerly Common Data Service) is the data platform underlying Dynamics 365 and Power Platform:

  • Contains standard and custom tables (entities)
  • Supports complex data types and relationships
  • Accessible via the Dataverse connector or OData feed
  • Row-level security from Dataverse can be leveraged
  • Power BI Dataflows

Dataflows provide reusable ETL logic in the Power BI Service:

  • Created using Power Query Online
  • Output is stored in Azure Data Lake Storage (Gen2)
  • Other reports and models can connect to dataflow outputs
  • Promotes data preparation reuse across the organization
  • Shared Semantic Models (Power BI Datasets)

Connect to an already-published Power BI dataset:

  • Live connection : No local model; all measures and tables come from the shared model
  • DirectQuery to a dataset : Build a composite model on top of a shared dataset
  • Promotes a "single source of truth" pattern
  • Changes to the shared model automatically propagate
  • Connecting to Azure Sources
  • Azure Data Lake Storage Gen2

For organizations using data lakes:

  • Connect using the Azure Data Lake Storage Gen2 connector
  • Authenticate with Azure AD or account key
  • Browse and import CSV, Parquet, or JSON files from the lake
  • Foundation for Direct Lake mode with Fabric
  • Azure Blob Storage

Simple cloud storage for unstructured data:

  • Connect via the Azure Blob Storage connector
  • Similar to Data Lake but without hierarchical namespace
  • Good for ad-hoc file imports
  • Connecting to Online Services
  • SharePoint Online

For team-based Excel and file sharing:

  • Use the SharePoint Online List connector for lists
  • Use the SharePoint Folder connector for files in document libraries
  • Requires SharePoint site URL and appropriate permissions
  • Supports automatic refresh through the Power BI Service
  • Web Connector

For public or API-based data:

  • Enter a URL to import HTML tables or API responses
  • Supports Basic , Web API , and Anonymous authentication
  • Advanced mode allows setting HTTP headers and URL parts
  • Useful for REST APIs that return JSON or XML
  • On the Exam

The PL-300 frequently tests:

  • Selecting the correct connector for a given data source
  • Understanding which connectors support DirectQuery vs. Import only
  • Knowing authentication options for different source types
  • Recognizing when to use a shared semantic model vs. creating a new one
  • Understanding the role of dataflows in enterprise BI