AB-730 AI Business Professional Questions and Answers

The correct completion is “cannot modify the settings of” because built-in agents such as the Researcher agent in Microsoft 365 Copilot are system-defined experiences. These agents are designed and managed by Microsoft to ensure consistent functionality, security, compliance, and alignment with responsible AI standards.

While users can interact with Researcher, provide prompts, and refine outputs during conversations, they cannot directly modify the core system settings or configuration of the built-in agent from Microsoft 365. Administrative configuration and deeper customization are available when creating custom Copilot agents through approved development tools, but default Microsoft-provided agents have controlled configurations.

Options such as modifying agent instructions, adding prompts, or deleting suggested prompts imply direct structural customization of the built-in agent interface, which is not supported in the standard Microsoft 365 Copilot experience.

This reflects a key generative AI governance principle in enterprise environments: system-level controls remain centrally managed to maintain data protection, compliance, and responsible AI implementation across the organization.

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Statements:

You can add a Microsoft Teams chat as a knowledge source to a Microsoft 365 Copilot agent.

Answer: Yes

You can add a Microsoft 365 Copilot conversation as a knowledge source to a Copilot agent.

Answer: No

You can use an image file, such as a JPG file, as a knowledge source for a Microsoft 365 Copilot agent.

Answer: NO

Microsoft 365 Copilot Agent Builder supports grounding agents in approved knowledge sources so responses are accurate and permission-trimmed. Teams chats can be used as a knowledge source: Microsoft Learn confirms you can ground an agent in Teams data, including chat messages (and optionally scope to specific chats), enabling the agent to reference collaboration context when answering questions.

A Copilot conversation itself is not a supported “knowledge source” option; knowledge sources are configured from items like SharePoint content, Teams, email, websites, uploaded files, and connectors—not prior chat transcripts.

Finally, JPG images are not supported as knowledge-source file types. Microsoft’s supported file types for agent knowledge include documents like .doc/.docx, .pdf, .ppt/.pptx, .txt, and Excel formats (.xls/.xlsx), but not image formats. Therefore, you cannot use a JPG as a knowledge source for a Copilot agent.

 You can continue the conversation in Copilot in Word.

Answer: Yes

 You can continue the conversation in Copilot for Teams.

Answer: Yes

 You can continue the conversation in Copilot in Outlook.

Answer: Yes

Microsoft 365 Copilot provides cross-application continuity across supported Microsoft 365 apps. When using the Researcher agent, users can continue or extend their work within applications such as Word, Teams, and Outlook.

In Word, users can incorporate research results directly into drafted documents, enabling seamless transition from research to content creation. In Teams, Copilot supports contextual collaboration by allowing insights from research to inform meetings or chats. In Outlook, findings can be referenced to compose informed and data-driven communications.

This interoperability is enabled by Microsoft Graph integration, which allows Copilot to securely access and contextualize enterprise data across applications while respecting organizational permissions.

These capabilities demonstrate effective management of prompts and conversations by maintaining workflow continuity, enhancing productivity, and supporting enterprise-grade collaboration powered by generative AI.

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The Describe tab is where you leverage generative AI to help build an agent by describing what you want the agent to do in natural language. Instead of manually configuring everything first, you provide the agent’s purpose, target users, tone, and the tasks it should perform (for example: “Create an onboarding agent that answers HR policy questions in a friendly tone and references SharePoint policies”). Copilot then uses that description to propose an initial agent setup—such as suggested behaviors, starter prompts, and structure—accelerating agent creation and improving consistency. By contrast, the Configure tab is typically used to fine-tune and finalize settings (such as knowledge sources, actions, and other parameters) after the agent concept is established. A Copilot notebook or Copilot page can help organize content and outputs, but they are not the primary place where generative AI “builds” the agent definition. Using the Describe tab reflects best practice: start with clear intent and constraints, then refine configuration for governance, accuracy, and usability.

Use the Analyst agent for Prompt 1 because it is optimized for structured data work: consolidating worksheets, performing calculations, sentiment breakdowns by category (product line), and producing visualizations. These tasks align with analysis-style workflows where quantitative processing and charting are required.

Use the Researcher agent for Prompt 2 because it is designed to synthesize information from multiple sources, including SharePoint content plus web grounding, and to produce a well-structured brief with citations. Researcher is best when you need evidence-backed insights, reputable sourcing, and an organized narrative with risks and gaps.

Use Chat for Prompt 3 because the goal is drafting a refined executive email based on an attached document with tone control and communication standards. Chat excels at business writing, rewriting, tone adjustment, and producing polished communications without needing the deeper research workflow or heavy data analytics specialization.

Microsoft 365 Copilot follows Microsoft’s enterprise data governance and privacy principles, which allow users to manage their own conversation history where appropriate. According to Microsoft AI Business Professional guidance, users can review and delete individual Copilot conversation histories directly from their personal account settings without requiring administrative intervention.

Option D is correct because the My Account portal in Microsoft 365 Copilot allows individual users to manage their activity history, including deleting specific conversations. This enables targeted removal of sensitive interactions while retaining other conversation data.

Option A is incorrect because the Copilot app itself does not provide full account-level activity management capabilities. Options B and C are administrative tools used for tenant-wide governance, compliance, retention policies, and eDiscovery. These portals typically require administrative privileges and are not intended for individual user self-service deletion of specific conversations.

Therefore, to delete a single confidential Copilot conversation without affecting other chats and without requiring administrator approval, the correct tool is the My Account portal in Microsoft 365 Copilot.

Microsoft 365 Copilot can use Microsoft Graph-grounded data (based on the user’s permissions) to help users work with Microsoft 365 content. This includes surfacing and summarizing information from Teams chats, so statement 1 is true. Copilot can also summarize and generate content related to Teams meetings (for example, meeting recap details, key discussion points, and follow-up actions when meeting artifacts such as transcript/notes are available), and it can answer questions and draft related content—so statement 3 is true.

Statement 2 is false because, while Copilot can draft emails and help prepare messages, it does not independently schedule and send emails automatically “based on conditions” as a native Copilot function. Conditional sending is typically handled through workflow automation tools (for example, Power Automate) and still requires appropriate configuration, permissions, and governance. In enterprise environments, this separation helps reduce risk by ensuring that automated outbound communication remains controlled, auditable, and compliant.

According to Microsoft AI Business Professional guidance for Microsoft 365 Copilot, grounding a response means ensuring the AI generates output based on specific, authoritative content rather than relying on generalized training data. When working with enterprise documents such as proposals, grounding is achieved by explicitly referencing the source material within the prompt.

Option C is correct because referencing the proposal content directs Copilot to use that specific document as the primary context for generating the response. Microsoft documentation emphasizes that effective prompts should include clear context and explicit references to relevant files, meetings, emails, or documents stored in Microsoft 365. This reduces hallucinations and ensures factual alignment with organizational data.

Option A is incorrect because relying on training data increases the risk of inaccurate or fabricated information. Options B and D improve prompt clarity but do not guarantee grounding in the proposal itself.

Therefore, explicitly referencing the proposal content is the most reliable and best practice method for ensuring grounded, accurate responses in Microsoft 365 Copilot.

Microsoft 365 Copilot provides structured ways to persist and reuse knowledge sources to avoid repeatedly uploading the same files.

Creating a notebook that references the five files (Option B) allows those documents to remain attached within a persistent workspace. Copilot can then consistently ground responses in those files without re-uploading them for every conversation.

Creating an agent with a defined knowledge source (Option D) also provides a reusable solution. By configuring the five files as part of the agent’s knowledge base, the agent can automatically reference them in future interactions.

Saving a prompt does not persist file attachments. Fine-tuning models is not part of standard Microsoft 365 Copilot user workflows. Uploading a ZIP file does not improve reference management and may reduce accessibility of individual documents.

Therefore, the correct solutions are to create a notebook that references the files and to create an agent with a knowledge source.

All three statements are false because generative AI responses are not guaranteed to be identical even when the prompt is the same. First, when grounded in the web, results can vary due to changing web content, different retrieved sources, or differences in how information is summarized at run time. Second, when grounded in your organization’s data, responses can change based on updates to files, emails, meetings, permissions, or which specific items Copilot retrieves as the most relevant context at that moment. Third, even when relying only on the model’s general knowledge, large language models are probabilistic: they may choose different wording, structure, examples, or emphasis across runs, especially when temperature/decoding settings and internal routing differ. In business scenarios, this means Copilot outputs should be treated as drafts that may require validation, and repeatability should be improved by adding precise constraints (cite specific sources, use fixed formats, specify exact sections, and request verbatim quotes where appropriate).