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FormateurAlaa Hady MBA, AI Engineer
Associé gérant, Advanced Management Solutions
Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 1 day · Level: Foundation to Intermediate (Level 100–200)
Objectives
- Understand how Copilot works across Power BI (Report creation, DAX generation, Executive Summaries, Q&A) and Microsoft Fabric (Dataflows, Notebooks, SQL, KQL).
- Master prompt engineering techniques tailored for analytical queries, DAX authoring, and narrative generation.
- Learn why Copilot fails on poorly modeled data—and how to optimize your semantic model so Copilot returns accurate answers.
- Understand Microsoft's data privacy, tenant governance, and capacity consumption (CU) rules for Copilot.
Outline and contents
- Architecture of Copilot in Microsoft Fabric & Power BI: Azure OpenAI integration, tenant boundaries, and why your data never trains public models
- Copilot for Report Consumers: generating executive page summaries, smart narrative visuals with custom prompts, and asking natural-language questions
- Copilot for Report Authors: auto-generating report pages from business prompts, refining layouts, and creating measure descriptions automatically
- Copilot in DAX Query View: writing, explaining, and refactoring complex DAX formulas using natural language
- Copilot across Fabric Engineering: generating Power Query M steps in Dataflows Gen2, T-SQL queries in Warehouse, PySpark code in Notebooks, and KQL in Eventhouse
- Optimizing Semantic Models for Copilot Accuracy: clean Star Schema, explicit column naming, synonyms, linguistic schemas, descriptions, and hiding technical keys
- Governance & FinOps: Tenant settings for Copilot, auditing AI outputs against source totals, and monitoring Copilot Capacity Unit (CU) usage
- Hands-on Prompting & Model Tuning Lab
Target audience
Business analysts, Power BI developers, analytics leaders, and power users adopting Microsoft Copilot capabilities.
Expected outcomes
Participants can accelerate report and DAX development with Copilot, prepare semantic models that yield trustworthy AI responses, and enforce responsible validation practices.
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