Data readiness and grounding for agents
Agent quality is capped by data quality. Most “the agent is wrong” scenarios on the exam are data problems, not model problems.
Prepare the data
Before grounding an agent, check that data is:
- Accurate and current: remove stale and duplicate records.
- Complete and consistent: same formats, same codes.
- Unbiased: representative of the customers or cases the agent serves.
- Governed: sensitivity labels and permissions are set.
Retraining is rarely the answer. Microsoft Copilot Studio and Microsoft 365 Copilot ground on your data at run time. They do not learn from it.
Where grounding data should live
| Source | Use it when |
|---|---|
| Dataverse | Structured business data shared by agents, Power Apps and Dynamics 365 |
| SharePoint and OneDrive | Documents and policies, reached through Microsoft Graph with the user’s permissions |
| Azure AI Search | Large custom knowledge bases for Microsoft Foundry agents |
| Fabric / OneLake | Analytical data shared across workloads |
For multiple AI systems, centralize in one source such as Dataverse rather than copying data per agent. Microsoft 365 Copilot grounds on tenant content through Microsoft Graph. It honours existing permissions, so fix SharePoint permissions rather than building a crawler.
Relationships between data
If an agent must know which departments connect to which policies, the data needs defined relationships. Dataverse table relationships, or a graph of entities, provide that. Flat files do not.
Security and residency
Role-based access should limit who can read grounding data and who can fine-tune a model. Use Dataverse security roles, Entra groups and Azure RBAC. Data loss prevention (DLP) policies in the Power Platform admin center control which connectors an agent may use together. If an agent calls a service in another geography, validate cross-region data movement and residency before deployment.
Exam traps
- “Retrain the agent” is wrong when the cause is bad grounding data.
- Sorting, size limits and translation do not make data suitable. Cleansing and bias checks do.
- Duplicating data into each agent’s own table creates drift. Centralize.
- Purview DLP and Power Platform DLP are different tools. Connector grouping is a Power Platform DLP policy.
- Adaptive cards change display, not answer accuracy.
Practice questions
Choose an answer, then review the explanation to check your understanding.
1. A Copilot Studio agent grounded on Dataverse returns inaccurate results. What is the first fix?
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Answer: B.Agents are not trained on your data. Poor answers usually come from poor grounding data, so cleanse and verify it.
2. An agent will analyze customer reviews to guide product decisions. How do you make sure the data is suitable?
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Answer: B.Skewed or unrepresentative data produces skewed insight. Check for bias, gaps and duplicates before ingestion.
3. Product catalog data is used by several agents and a Power Apps app. What do you recommend?
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Answer: B.One governed source in Dataverse keeps agents consistent and avoids duplicate copies.
4. An agent uses a connector to an Azure OpenAI resource in a different region than the environment. What do you check before deployment?
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Answer: B.Check where data is processed and whether cross-region data movement is allowed by policy and the environment settings.