Overview
Artificial Intelligence is changing how business users interact with data, develop analytical solutions, and generate business insights. By connecting Power BI semantic models with AI tools such as Claude, GPT models, and Microsoft Copilot, users can work with business data through natural language commands and AI-assisted workflows.
In this module, learners will explore how Model Context Protocol (MCP) connects Power BI semantic models with AI applications. They will learn how to use natural language commands to work with semantic models, generate documentation and DAX, build AI agents, and apply prompt engineering techniques for Power BI.
The module also covers connecting Power BI semantic models with Excel PivotTables using Copilot and exploring machine learning and predictive analytics with AI tools.
The focus is on combining business intelligence with AI capabilities to simplify data analysis, improve productivity, and support more informed business decisions.
This special class provides exposure to emerging AI and agentic capabilities in the Microsoft data ecosystem. It also reinforces selected Power BI concepts relevant to the Microsoft PL-300: Power BI Data Analyst certification examination.
What Will Be Covered
9.1 Connect Power BI Semantic Models with Claude and GPT Models Using Native Power BI Model Context Protocols
Understanding Model Context Protocol (MCP) and its role in connecting AI applications with external tools and data.
Exploring how Power BI MCP capabilities work with semantic models.
Understanding how compatible AI clients can connect to Power BI through MCP.
Connecting Power BI semantic models with supported Claude and GPT-based AI environments.
Exploring how AI tools can interact with semantic model structures and metadata.
Using natural language commands to work with Power BI semantic models.
Generating and reviewing DAX expressions using natural language instructions.
Creating semantic model documentation with AI assistance.
Exploring AI-assisted semantic model development and optimization.
Understanding how AI-native semantic models can support natural language data interaction.
Reviewing AI-generated model changes, documentation, and DAX before applying them.
9.2 Create Enterprise AI Agents and Agentic Frameworks Using Power BI MCP Native Data Agents and Fabric Connectors
Understanding AI agents and agentic frameworks in enterprise environments.
Exploring how AI agents interact with business data and connected services.
Understanding the role of Power BI MCP in connecting AI agents with semantic models.
Exploring Microsoft Fabric data agents and their capabilities.
Understanding how Fabric connectors support access to enterprise data.
Connecting AI agents with relevant business data sources.
Exploring how agents can retrieve information and respond to business questions.
Understanding how natural language interaction supports enterprise data analysis.
Exploring practical business scenarios for AI-powered data agents.
Understanding permissions, access control, and governance when connecting AI agents to business data.
9.3 Prompt Engineering for Power BI
Understanding prompt engineering and its role in AI-assisted data analysis.
Learning how to write clear and structured prompts for Power BI tasks.
Using natural language prompts to generate DAX expressions.
Creating prompts for semantic model documentation.
Using prompts to understand tables, relationships, measures, and business metrics.
Refining prompts to improve the relevance and accuracy of AI-generated responses.
Using prompts to analyse business performance and KPI results.
Exploring prompt patterns for reporting, data exploration, and troubleshooting.
Reviewing and validating AI-generated outputs before using them in business reports.
9.4 Connect Power BI Semantic Models with Excel Pivots with Copilot
Understanding the connection between Power BI semantic models and Excel.
Exploring how Excel PivotTables can use published Power BI semantic models.
Connecting Excel to Power BI data models through Analyze in Excel.
Exploring how Copilot can support analysis of connected business data.
Using PivotTables to summarize and explore business information.
Asking natural language questions about connected data using supported Copilot experiences.
Understanding how semantic models provide a consistent source for business analysis.
Exploring how Excel and Power BI work together for reporting and decision-making.
Using AI-assisted analysis to identify useful business insights.
9.5 Apply Machine Learning Techniques and Predictive Analytics Using Copilot, Claude, and GPT Models
Understanding the fundamentals of machine learning and predictive analytics.
Exploring how historical business data can support predictive analysis.
Understanding the difference between descriptive and predictive analytics.
Identifying business use cases for forecasting and trend analysis.
Exploring how AI tools can assist with data exploration and predictive analysis.
Using Copilot, Claude, and GPT models to support analytical workflows.
Exploring AI-assisted interpretation of trends, patterns, and business performance.
Understanding how predictive insights can support business planning.
Reviewing AI-generated predictions and analytical outputs.
Understanding the importance of data quality, validation, and business context when interpreting predictive results.
Skills You Will Gain
By completing this module, learners will develop practical skills in the following areas:
1. Power BI MCP and AI Integration
Understand the purpose of Model Context Protocol.
Connect compatible AI environments with Power BI through supported MCP capabilities.
Work with Power BI semantic models using natural language commands.
Generate and review DAX expressions and model documentation with AI assistance.
Understand AI-assisted semantic model development.
2. Enterprise AI Agents and Fabric Data Agents
Understand enterprise AI agents and agentic frameworks.
Explore how Power BI MCP connects AI agents with business data.
Understand the role of Microsoft Fabric data agents.
Identify opportunities to use AI agents for enterprise data access and analysis.
Understand access control and governance considerations for connected AI systems.
3. Prompt Engineering for Power BI
Write structured prompts for Power BI-related tasks.
Generate DAX expressions using natural language instructions.
Create prompts for data exploration and semantic model documentation.
Refine prompts to improve AI-generated results.
Validate AI responses before applying them to business reporting.
4. AI-Assisted Excel and Power BI Analysis
Connect Excel to published Power BI semantic models.
Analyse business data using PivotTables.
Understand how Copilot supports connected data analysis.
Use natural language to explore business information through supported AI experiences.
Work with consistent business metrics across Excel and Power BI.
5. Machine Learning and Predictive Analytics
Understand basic machine learning and predictive analytics concepts.
Explore historical data for trends and patterns.
Use AI tools to support predictive analysis.
Interpret AI-assisted analytical results.
Understand the importance of validating predictions before using them for business decisions.
Learning Outcomes
By the end of this module, learners should be able to:
Connect compatible AI tools with Power BI semantic models and interact with business data using natural language.
Use AI assistance to understand business metrics, explore data relationships, and generate analytical queries.
Generate and review DAX expressions and semantic model documentation using natural language instructions.
Build a better understanding of cross-functional business performance by asking questions across connected departmental datasets.
Explore how marketing activities influence sales, how IT performance relates to HR productivity, and how different business functions contribute to organizational results.
Understand how enterprise AI agents and Microsoft Fabric data agents can support business data access and analysis.
Apply prompt engineering techniques to investigate business questions and obtain relevant insights from connected data.
Use Copilot to explore Power BI semantic models through Excel PivotTables and connected data.
Apply AI-assisted machine learning and predictive analytics to identify patterns, understand historical performance, and explore future business trends.
Validate AI-generated calculations, explanations, and predictions before using them for business decisions.
Identify opportunities to use AI and business intelligence together to improve reporting, analysis, and management decision-making.
Microsoft PL-300 Exam Alignment
This module focuses on emerging AI capabilities, MCP integrations, and agentic workflows. These topics extend beyond the core scope of the Microsoft PL-300: Power BI Data Analyst certification examination.
However, the module reinforces selected competencies related to semantic models, DAX, data analysis, and AI-assisted Power BI development.
Model the Data – 25–30% of the examination
Microsoft's study guide identifies several competencies within this area.
1. Create and Manage Semantic Models
Candidates should be able to:
Design and configure semantic models.
Create and manage relationships between tables.
Configure model properties.
Create measures using DAX.
Optimize semantic models for performance.
Understand model structure and data organization.
2. Write DAX Expressions
Candidates should be able to:
Create calculated measures.
Use DAX functions to perform calculations.
Apply filter context and row context.
Use DAX to support business calculations and analytical requirements.
Validate DAX expressions and their results.
Visualize and Analyze the Data – 25–30% of the examination
3. Identify Patterns and Trends
Candidates should be able to:
Analyse data to identify trends and patterns.
Identify outliers and anomalies.
Use analytical features to investigate business performance.
Apply forecasting and other supported analytical capabilities.
Interpret analytical results to support business decisions.
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