Managing and Scaling Power BI Semantic Models with MCP Server

Explore how to maximize the capabilities of the Power BI MCP Server to efficiently manage and scale semantic models. Start by reviewing how to prepare and interact with the server using VS Code and AI-driven prompts. Learn how to benchmark DAX queries across multiple models to predict and optimize semantical efficiency. Note methods for refactoring queries and measures, and efficiently rename model labels in bulk for consistent updates. Translate entire semantic models into different languages, expanding their reach and applicability for global teams. Generate detailed Markdown documentation, ensuring each element of your model is well-documented and easily scalable. This course is ideal for data analysts and data scientists working in large organizations. Whether you're new to MCP Server or aiming to refine your skills, this course can equip you to transform your workflows and improve documentation practices.

Learning objectives
Prep and interact with the Power BI MCP Server in VS Code.
Benchmark DAX queries between multiple models.
Refactor DAX queries and measures with semantic model parameters.
Analyze model naming convention and bulk rename of labels.
Add model descriptions.
Translate semantic model into another language.
Generate Markdown documentation that includes extensive descriptions and diagrams.