Start with a small Fabric project: create a workspace, build a lakehouse, load and check sample data, create a semantic model, then make a report. Copilot is a separate step: for the documented Power BI experiences, a Pro or Premium Per User license alone is not enough. Microsoft lists eligible paid capacity, administrator enablement, and a supported region among the requirements, which vary by experience and can change.
Build a first Fabric analytics project
Use a nonproduction workspace and sample data while learning. The workflow below follows Microsoft’s end-to-end tutorial: data lands in a lakehouse, is prepared for analysis, and is exposed through a semantic model and report.
- Create or open a workspace. You may need a Fabric administrator to provide an eligible learning environment or workspace access.
- Create a lakehouse and load sample data. A lakehouse provides a place to store data for the project. Follow a lakehouse tutorial if you need a guided introduction to ingestion and transformation.
- Inspect and prepare the data. Check columns, types, missing values, and whether the tables represent what you expect. For a repeatable ingestion process, the end-to-end tutorial uses Dataflows Gen2 and can add a pipeline to orchestrate refresh.
- Create a semantic model. Select the tables needed for analysis and define relationships that reflect the facts and dimensions in the data. Microsoft documents Direct Lake for lakehouse models; Import and DirectQuery are also available in relevant cases. Choose based on your source, refresh needs, and reporting requirements rather than assuming one mode fits every project.
- Create a report and verify it. Build a few visuals from the model, then check key totals and relationships against the source data before relying on any explanation or generated analysis.
The right first workflow depends on what you want to learn. A lakehouse tutorial is useful for exploring ingestion and transformation; add Dataflows Gen2 or a pipeline when repeatable loading matters. If the main goal is reporting, prioritize a sound semantic model and report. Microsoft’s tutorials document these paths but do not establish a universal best architecture.
What you need before enabling Copilot
For the documented Power BI Copilot experiences, Microsoft lists paid Fabric F2-or-higher or Power BI Premium P1-or-higher capacity, an enabled administrator setting, and a supported region. A Power BI Pro or Premium Per User (PPU) license on its own does not provide the required organizational capacity. Microsoft also says the documented requirements do not support sovereign clouds. Check the current requirements for the specific Copilot experience because eligibility can differ and change. See Microsoft’s Copilot for Power BI overview and Fabric Copilot configuration guidance.
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Administrator setup checklist
- Identify the experience. Confirm the current capacity and regional requirements for the feature users intend to use.
- Review tenant settings. In the Fabric admin portal, review and enable the applicable Copilot setting, limiting it to intended users or security groups where appropriate. Microsoft explains how to enable and configure Copilot.
- Review data-processing geography. Depending on location and settings, the administrator may need to allow data sent to Azure OpenAI to be processed outside the capacity’s geographic region or compliance boundary. Treat this as a privacy and compliance decision, not merely a technical toggle.
- Check delegated settings. If tenant settings are delegated, the capacity administrator may also need to enable Copilot for that capacity.
- Assign the workspace and grant access. Copilot-enabled items must be associated with supported capacity, and users need appropriate workspace access. In Power BI Desktop, select a compatible workspace as described in Microsoft’s enablement guidance.
- Roll out deliberately. Start with a limited group and monitor capacity use; Microsoft cautions that broad enablement without planning can raise utilization.
These capacity requirements concern organizational Copilot access, not the basic learning workflow. A learner may follow Microsoft’s Fabric tutorials in an eligible environment without treating Copilot as a prerequisite.
Prepare the semantic model so Copilot has useful context
Copilot works against the data and definitions available to it; it is not a substitute for a well-designed model or verification. Microsoft warns that unprepared data, models, or users can lead to inaccurate or misleading responses. Its guidance recommends simplifying the schema, supplying verified answers, and writing AI instructions. The semantic-model guidance also describes testing with Copilot and marking a model approved for Copilot; some of these capabilities are in preview.
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A practical preparation loop
- Simplify the schema. Keep the model understandable and remove confusing or unnecessary elements where appropriate.
- Provide trusted definitions. Add verified answers and AI instructions that explain business terms and how measures should be interpreted.
- Test representative questions. Compare responses with known totals, definitions, and source data. Correct the model or instructions when results do not agree.
- Use approval features only where available. Check the current status and scope of model approval features before relying on them in a production process.
Where Fabric Copilot can help
Copilot features are workload-specific, so availability in one part of Fabric does not guarantee the same feature set elsewhere. Microsoft’s Data Factory documentation describes natural-language assistance for Dataflow Gen2 and pipelines, including transformation-query help, pipeline generation and summaries, and troubleshooting recommendations. Check the current feature status, including whether a capability is in preview, before building it into a production design.
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