Explore where Copilot appears and what types of tasks it can support. Greg walks through the broader Copilot experience and explains how it connects to user workflows. You’ll learn how Copilot can assist with analysis, productivity, and data-driven interaction when the right information is available.

Lab 1 Start

• Begin the first lab and prepare to explore core Copilot capabilities in the data experience. Greg introduces the lab goals, what learners will work on, and how this activity fits into the overall course. This module helps transition from overview concepts into hands-on practice.

Exploring Capabilities

• Start the second lab with a focus on improving the structure and usability of the data model. Instructor will be introduce to the next phase of preparation and explain how better naming, descriptions, categories, and related settings can improve the user experience. This module sets up the semantic clean-up work that follows.

Renaming

Lab 3 Start

• Begin the third lab, focused on improving AI behavior through instructions and verified answers.  Instructor will introduce the next set of configuration steps and explains how they help guide the user experience. This module sets up the shift from model preparation to AI response quality.

• Learn how AI instructions can guide the way Copilot or a data agent responds to user questions. Learn how instructions help define behavior, expectations, and response style. This module begins the process of shaping the AI experience so answers are more useful and aligned with the intended scenario.

• Continue refining AI instructions with additional guidance and examples. Instructor builds on the previous module by showing how more specific instructions can improve consistency and reduce ambiguity. This module helps learners understand how thoughtful instruction design supports better user outcomes.

• Explore verified answers and how they can improve trust in AI-assisted data experiences. Learn how verified responses can help guide users toward approved, reliable answers for common or important questions. This module helps learners understand how to combine flexibility with governance.

• Start the fourth lab, focused on visualizations and standalone answer experiences. The instructor will introduce the goals for this section and explain how the next set of activities expands beyond model configuration. This module prepares learners to connect data interaction with clearer outputs and user-facing experiences.

• Learn how visualizations support the Chat With Your Data experience by making insights easier to interpret. Learn how visuals can complement natural-language answers and help users better understand trends, comparisons, and results. This module reinforces the connection between AI responses and traditional data storytelling.

• Explore standalone verified answers and how they can provide approved responses outside a broader report or model interaction. The instructor will show how this approach can help users access trusted information quickly. This module helps learners think about verified answers as reusable, governed knowledge assets.

• Learn how context switching affects the user experience when moving between data, questions, answers, and supporting tools. Learn why maintaining context matters when users are exploring data conversationally. This module helps learners understand the importance of designing smoother, more connected workflows.

• Explore how Chat With Your Data experiences can improve productivity for users and teams. Learn how natural-language questions, guided answers, and better-prepared data can reduce time spent searching, interpreting, and manually building analysis. This module focuses on the practical business value of the overall approach.

• Begin the fifth lab, focused on creating and testing a data agent. Greg introduces the goal of building an agent-based experience and explains how the previous preparation work supports this next step. This module transitions the course from semantic preparation into agent creation.

• Create and test a data agent that allows users to interact with curated data through natural language. The instructor walks through the core build process and explains how to validate that the agent responds as expected. This module helps learners move from concept to a working agent experience.

• Learn how instructions shape the behavior of a data agent. And how to guide the agent’s responses, define expectations, and keep the experience aligned with the intended use case. This module helps learners improve agent reliability and usefulness through clearer configuration.

• Connect a Lakehouse source so the agent can use curated Fabric data in its responses. Learn how adding a source expands what the agent can answer and why source selection matters. This module helps learners connect the agent to the data foundation needed for meaningful interaction.

• Explore how a Lakehouse functions as a data source for a chat-based analytics experience. Learn what the agent can use from the Lakehouse and how this supports natural-language exploration. This module helps learners understand the role of curated lakehouse data in building trustworthy data agents.

• Learn how to publish and share the data agent so others can use it, and the steps involved in moving from a test experience to something accessible for users. This module helps learners think about deployment, audience, and usability beyond the initial build.

• Test the data agent in a standalone experience to see how it performs outside the build environment. The instructor demonstrates how to review responses, validate behavior, and identify areas for improvement. This module helps learners confirm that the agent is ready for real user interaction.

• Understand the limitations of Chat With Your Data and data agent experiences. The instructor explains where AI may struggle, why source quality and model design still matter, and what users should be cautious about. This module helps learners use the technology responsibly and set realistic expectations.

• Review the tenant settings that can affect Copilot, data agents, and related AI experiences. Learn why administrative configuration matters for enabling features, controlling access, and supporting governance. This module helps learners understand that successful implementation depends on both the build and the environment around it.