Effective prompting in Microsoft 365 Copilot requires two critical components: clear instructions and grounding in authoritative content. Microsoft guidance emphasizes that prompts should explicitly state the desired output format, tone, length, and purpose. In this case, specifying “a bulleted list of three to five concise, simply worded key points” represents concise instructions, making option B essential.

Additionally, grounding the response in a specific knowledge source—such as attaching or referencing the proposal document—ensures factual accuracy and reduces hallucination. Option D ensures Copilot uses the actual proposal content rather than relying on generalized model knowledge.

Profiles and stakeholder contact details are unrelated to summarization requirements. Therefore, the correct selections are B and D.

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To generate an effective monthly summary with Copilot in Excel, Microsoft prompting guidance emphasizes two core elements: clear intent and expected output structure . First, you should state the goal explicitly—such as “summarize total sales by month based on OrderDate and Amount, and highlight month-over-month changes.” This directs Copilot to the correct aggregation and analysis behavior (Option D).

Second, specifying the desired format improves usability and reduces ambiguity. For example, requesting “a table with Month, Total Sales, Number of Orders, and Average Order Value” or “a bulleted executive summary plus a small summary table” ensures Copilot returns results in a format you can immediately use (Option A).

A specific cell range (Option B) is usually unnecessary when the data is already in a recognized Excel table, because Copilot can work from the table context. The workbook file name (Option C) does not help analysis. Providing a formula (Option E) is not required because Copilot can compute monthly totals; formulas are optional if you want a specific calculation method, but they are not the best prompt ingredients for an effective summary.

Therefore, the best answers are A and D .

 When Microsoft 365 Copilot searches for content on the web, it uses the Bing search service.

Answer: Yes

 When Microsoft 365 Copilot searches for content on the web, it uploads the entire conversation history into the search request.

Answer: No

 When Microsoft 365 Copilot searches for content on the web, the query is anonymized and stripped of any user-identifiable information.

Answer: Yes

Microsoft 365 Copilot can use web grounding when a response requires current or public information. For web search, Copilot uses the Bing search service to retrieve relevant web results, so Statement 1 is Yes . This allows Copilot to supplement its response with up-to-date internet content when web grounding is enabled or selected.

Statement 2 is No because Copilot does not send the entire conversation history to Bing as a web search request. Instead, Copilot generates a search query based on the user’s prompt and relevant context. Sending the complete conversation would create unnecessary privacy and data exposure risk, which would conflict with Microsoft’s enterprise data protection model.

Statement 3 is Yes because Microsoft 365 Copilot is designed to protect user and tenant information during web grounding. Search queries are handled in a privacy-preserving way, with user-identifiable and tenant-identifiable information removed before the query is sent to Bing.

Microsoft 365 Copilot Chat is included at no additional cost for users with qualifying Microsoft 365 subscriptions such as Business Basic, Business Standard, Business Premium, E3, and E5. Therefore, users without a Microsoft 365 Copilot add-on license can still access Copilot Chat.

Copilot Chat allows users to upload documents from their local device within a session for summarization, analysis, and drafting assistance. However, advanced enterprise-integrated features—such as creating agents that access organizational SharePoint folders—require a Microsoft 365 Copilot license.

Agent creation and enterprise data grounding leverage Microsoft Graph integration and are considered premium Copilot capabilities tied to licensed Copilot services.

This distinction reflects Microsoft’s licensing model: baseline AI chat access is included with qualifying subscriptions, while deep Microsoft 365 app integration and enterprise data agents require the additional Copilot license.

Temporary chat in Microsoft 365 Copilot is designed for privacy-focused, non-persistent interactions. When a user initiates a temporary chat session, the conversation is not saved to Copilot chat history and is not retained for future reference within the standard conversation history interface.

Once the session ends or a new chat begins, the prompts and responses from that temporary session are deleted immediately. They are not moved to OneDrive, stored in a notebook, or retained for a delayed deletion period.

This feature supports secure and confidential usage scenarios where users may want to explore ideas, draft sensitive content, or test prompts without creating a stored record in their activity history.

From a generative AI governance perspective, this reflects Microsoft’s design principle of providing configurable persistence levels, balancing productivity with enterprise security, compliance, and user privacy controls.

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.

When you use Microsoft 365 Copilot Chat without a Microsoft 365 Copilot license, Copilot Chat operates in a “general” mode rather than a Microsoft Graph-grounded work mode. In this configuration, Copilot does not have the entitlement to access and reason over your organization’s protected Microsoft 365 content (such as SharePoint sites, OneDrive files, Outlook mail, Teams messages, or other tenant resources). It also does not connect directly to your organization’s internal databases unless separate, explicitly configured integrations exist.

Instead, Copilot relies on three primary sources to generate a response: (1) the prompt context you provide in the current request (what you ask, plus any text you paste), (2) the conversation history within the current chat thread to maintain continuity, and (3) internet search data (when web grounding is available) to supplement general knowledge with up-to-date public information.

Therefore, the correct selections are C (prompt context), E (internet search data), and F (conversation history) , while organizational Microsoft 365 data and Teams chat data are not used without the Copilot license.

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.

Microsoft 365 Copilot is designed to be helpful by using work context —for example, the files you have access to, recent activity, meetings, emails, and SharePoint/OneDrive content—to suggest relevant prompts and help you start tasks faster. It also uses this context to augment your prompt before it is sent to the LLM. This is the grounding approach (often described as retrieval-augmented generation): Copilot retrieves relevant organizational content you’re permitted to access and adds it as supporting context so responses are accurate and business-relevant. However, Microsoft 365 Copilot does not use your organization’s contextual data to train the underlying foundation model . That separation is critical for enterprise privacy and compliance: your prompts, responses, and tenant data are used to generate the answer for your session and permissions, but are not used to improve or retrain the base LLM. This approach supports responsible AI, protects confidential business information, and ensures outputs respect access controls.

Copilot in Outlook is designed to reduce time spent reading and writing email by using AI to summarize message threads and generate drafts grounded in the current email context. Microsoft guidance for Copilot in Outlook highlights core capabilities such as drafting replies that reflect the conversation tone and content, and summarizing email threads or inbox content so users can quickly understand what requires action.

Option B is correct because Copilot can generate a draft reply based on the context of the email conversation, helping you respond faster while keeping the response aligned to what was discussed. Option C is correct because Copilot can summarize emails—such as unread messages or a thread—by extracting key points, requests, decisions, and suggested next steps, which supports rapid triage.

Option A is not a Copilot task; inserting or customizing an HTML signature is a standard Outlook configuration feature. Option D is also a traditional Outlook management function and is not an AI drafting/summarization capability.

Therefore, the two tasks you can achieve with Copilot in Outlook are drafting responses and summarizing unread emails .

Agents created in the Microsoft 365 Copilot app are primarily designed for use within the Copilot Chat experience and related Copilot surfaces, not directly embedded inside individual applications like Word. Therefore, such agents are not automatically available in Microsoft Word, making Statement 1 incorrect.

However, these agents are designed for collaboration and reuse within an organization. Microsoft guidance confirms that agents can be shared with other users in the same tenant , allowing teams to leverage common tools, prompts, and knowledge sources. Hence, Statement 2 is correct.

For advanced automation scenarios involving complex workflows and third-party connectors, Microsoft provides Copilot Studio rather than the standard Copilot app. The Copilot app focuses on lightweight agent creation using natural language, files, and simple configurations. It does not support complex orchestration with external connectors. Therefore, Statement 3 is incorrect.

Thus, the correct answers are No, Yes, No .

Enterprise data protection (EDP) (often referred to as commercial data protection) is the capability that ensures Microsoft 365 Copilot handles organizational data under enterprise security and privacy guarantees. With EDP, Copilot processes prompts and responses within the customer’s Microsoft 365 boundary, respecting tenant isolation and identity-based access controls. This means Copilot only retrieves and uses data that the signed-in user is permitted to access, and the data remains governed by Microsoft 365 compliance features (such as retention, eDiscovery, audit, and Purview controls). Critically, EDP ensures that your organization’s prompts and the retrieved business content are not used to train the underlying AI foundation models. This is essential for protecting confidential business information and meeting regulatory and contractual requirements. The other options (Common Data Model, sensitivity labels, and Zero Trust) support data structure, classification, and security posture, but they do not specifically represent the Copilot guarantee that tenant data stays protected and is not used for model training.

Microsoft 365 Copilot agents are designed to integrate with Microsoft 365 data sources and provide flexible, natural-language-driven configuration. Agents can be connected to organizational data such as SharePoint, OneDrive, and Exchange. Since shared mailboxes are part of Exchange Online and accessible through Microsoft Graph (subject to permissions), agents can be configured to search shared mailboxes, making Statement 1 correct.

Microsoft also enables natural language creation of agents , allowing users to describe what the agent should do, its purpose, and behavior using conversational instructions rather than requiring code. This simplifies agent creation and aligns with Copilot’s low-code/no-code approach, so Statement 2 is correct.

However, agents cannot use other agents as knowledge sources. Knowledge sources are limited to structured content such as files, sites, and data repositories. Agents operate independently rather than being nested or chained as data sources, making Statement 3 incorrect.

Thus, the correct answers are Yes, Yes, No .

When creating an agent in the Microsoft 365 Copilot app using a template, administrators or authorized users can configure specific behaviors and knowledge sources for the agent. Therefore, it is correct that you can choose which knowledge sources the agent can use. This ensures proper grounding in approved Microsoft 365 data such as SharePoint sites, OneDrive folders, or other organizational repositories. Controlling knowledge sources supports responsible AI principles, including data security, relevance, and compliance.

Agents created within Microsoft 365 are designed primarily for internal organizational use. As a result, sharing the agent with users inside the organization is supported, enabling collaboration and productivity improvements across teams.

However, sharing the agent with external users is not supported by default. External sharing would introduce security and compliance risks, so Microsoft enforces tenant-level boundaries to protect enterprise data.

These capabilities reflect best practices in managing AI prompts and agents securely within enterprise environments.

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).

suggested prompts

In Microsoft 365 Copilot, suggested prompts are designed to help users get started quickly and interact with an agent effectively. Microsoft guidance for Copilot agents emphasizes that many users struggle most at the beginning—knowing what to ask, how to phrase requests, and how to take advantage of an agent’s purpose. Suggested prompts address this by providing ready-to-use examples that demonstrate the best ways to engage the agent for its intended scenarios.

Suggested prompts also act as lightweight “guardrails” for good prompting. They show users the expected input patterns, the types of tasks the agent can perform, and the kinds of outputs they can request. This accelerates adoption and improves the quality of results by steering users toward well-structured requests and appropriate context.

By contrast, capabilities describe what an agent can do (tools/features), instructions define how the agent should behave, and agent settings control configuration. None of those are primarily meant to help users begin conversations through example starters.

Therefore, the correct completion is suggested prompts .

Microsoft 365 Copilot does not train the underlying large language model on your organization’s tenant data. Instead, it uses your organization’s data at run time to ground responses (for example, by retrieving relevant emails or documents you have permission to access). This separation protects confidentiality and supports enterprise compliance.

If a mailbox (or specific folders) is shared with you and your account has the required permissions, Copilot can summarize emails from that shared content because Copilot respects Microsoft 365 access controls (“permission trimming”).

Copilot also relies on Microsoft 365’s established security and compliance framework—identity, permissions, and policy enforcement—so it only surfaces content you’re authorized to see. This ensures Copilot’s outputs remain aligned with organizational governance, information protection, and least-privilege access principles.