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| Section | Objectives |
|---|---|
| Topic 1: Build and Configure AI Agents in Copilot Studio | - Creating and configuring copilots and topics - Configuring actions, plugins, and connectors - Using generative AI and prompt engineering in Copilot Studio |
| Topic 2: Governance, Security, and Compliance | - Environment management in Power Platform - Data protection and responsible AI practices - Managing access control and authentication |
| Topic 3: Integrate External Systems and Data Sources | - Integrating Dataverse and Power Platform components - Using Power Automate for workflow orchestration - Connecting Azure services and APIs |
| Topic 4: Deploy, Monitor, and Optimize AI Agents | - Monitoring performance and analytics - Publishing and deploying copilots - Iterative improvement and lifecycle management |
| Topic 5: Plan and Design Copilot Studio AI Agents | - Selecting Copilot Studio capabilities and architecture approach - Requirements analysis for AI agent solutions - Designing conversation flows and agent behavior |
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NEW QUESTION # 40
Hotspot Question
A company uses an agent flow that occasionally requires human input before continuing execution.
Some automated actions must pause until a human provides a decision or additional information.
The flow must be configured to:
- Capture a human response for use in later steps.
- Continue processing within the same flow run after the response is
submitted.
- Wait for a manual decision before proceeding.
You need to configure a human-in-the-loop agent flow.
Which setting should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Use the action's response outputs in subsequent steps.
The correct configuration is to use the action's response outputs in subsequent steps.
Data Persistence: Pausing for human intervention (often via a Power Automate "Approval" or
"Wait for an Action" step) generates dynamic output variables based on the human's decision.
Flow Continuity: To use the decision or additional information later, the flow must explicitly capture these response outputs and map them to subsequent steps or variables within Copilot Studio.
Box 2: Allow the action to wait for and return a response before continuing The valid configuration is to allow the action to wait for and return a response before continuing.
Same Flow Run: This setting explicitly pauses the current execution thread and waits for the human interaction to complete. Once the response is submitted, it resumes processing exactly where it left off within the same run.
Box 3: Add a human approval action.
The correct configuration is to add a human approval action.
Built-in pausing: An approval action natively pauses the workflow execution.
State preservation: The flow holds its current state and waits until the user selects an outcome (e.g., Approve or Reject). Simplicity: It does not require complex logic to save data and restart the process from scratch.
Reference:
https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/introducing-request-for-information-in-copilot-studio-agent-flows/
NEW QUESTION # 41
Drag and Drop Question
You run the same fixed test set three times in Copilot Studio.
During evaluation, you observe the following:
- The same interaction fails in all three runs.
- Score values range from 0.58 to 0.61.
- The reasoning states that the response partially matches the expected answer.
- The knowledge source that is used is internal documentation.
- No tools are invoked.
You need to determine which conclusions are supported based on the evaluation results.
Which conclusions should you make? To answer, move the appropriate conclusions to the correct evaluation results. You may use each conclusion once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: A pattern failure across repeated runs.
The correct conclusion concerning evidence of a recurring issue is a) a pattern failure across repeated runs.
When the exact same interaction fails across three repeated runs, it establishes a systemic, predictable pattern rather than an isolated, one-time anomaly.
Box 2: The response partially matches the expected answer.
Based on the evaluation criteria in Microsoft Copilot Studio, the most definitive and direct conclusion that can be made concerning response quality is that the response partially matches the expected answer.
Direct Evidence: The evaluation results explicitly state in the reasoning that the "response partially matches the expected answer." This directly provides a qualitative conclusion regarding the quality of the generated response.
Score Alignment: The semantic similarity or quality score values ranging from 0.58 to 0.61 align with a partial match. In AI evaluation metrics, a perfect match is represented by 1.0, while values in the 0.6x range indicate that the core context was captured but lacked full completeness or exactness.
Box 3: Use of a connected knowledge source during the interaction
Because generative AI outputs are non-deterministic, running the same test set across a dynamic environment can yield varying scores. In Microsoft Copilot Studio, observing scores consistently between 0.58 and 0.61 points to a fundamental limitation with the underlying knowledge source.
The evaluation points to option use of a connected knowledge source during the interaction.
Because no tools are invoked, the agent heavily relies on generative search (retrieval-augmented generation) over internal documentation. The score range and reasoning indicate that the agent successfully retrieves a document but relies on a probabilistic model to paraphrase or assemble the response, which results in a "partial match" rather than an exact extraction.
Incorrect:
In contrast, specific underlying causes require explicit tool invocations or system error logs to diagnose, which are not present here.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-agent-evaluation-results
NEW QUESTION # 42
An agent in Copilot Studio must complete tasks by automating interactions with a legacy desktop application that does not use APIs.
During testing, the builder must confirm that the automation runs in a supported execution context and can inspect how each automation step executes during conversations.
To meet the business needs, the solution must:
- Execute UI automation in a supported, platform-managed environment.
- Provide visibility into how automation steps run during agent
conversations.
You need to configure the agent based on the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
[B]
The agent runtime view / Activity page is where a builder checks real-time actions and logs. It explicitly provides visibility into how the orchestration engine is making decisions, running individual automation steps, and processing conversation flows.
[C]
Computer use is the precise feature in Copilot Studio designed to automate legacy applications that do not have APIs. Turning it on allows the agent to interact with user interfaces using a vision-based reasoning layer in a platform-managed environment.
Incorrect:
[Not A]
Publishing the agent makes the bot available to end-users on live channels. While it applies settings to production, publishing is a deployment step rather than a diagnostic or setup step used to confirm execution context or inspect granular automation steps while a builder is actively testing.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/configure-where-computer-use-runs
NEW QUESTION # 43
Users can add content to a paragraph field in a team-managed project, but they cannot @mention other users.
Identify the cause.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From [Atlassian Jira Cloud Documentation] The cause is the Jira Browse users and groups global permission. Atlassian states that users with this permission can view user names when working with user-picker fields or @mentioning users. Without it, Jira cannot provide the affected users with the complete user directory required to search for and select another person through an @mention.
The fact that users can enter and save ordinary text in the paragraph field confirms that the field is present, editable, and available on the applicable work-item layout. Therefore, field settings and screen settings are not responsible. Notification settings determine how users receive updates after an event and do not control whether names appear in the mention picker.
Project permissions and issue-level security can affect whether a particular person can access the project or work item. However, a general inability to search for or mention other users indicates that the initiating users lack the site-wide user-browsing capability.
A Jira administrator should review Settings โ System โ Global permissions and grant Browse users and groups to an appropriate group containing the affected users, subject to the organization's privacy policies.
Relevant documentation topic: Jira Cloud administration โ Global permissions โ Browse users and groups. See What are global permissions, and what do they control?.
NEW QUESTION # 44
You need to integrate Fabrikam Inc. ' s existing Foundry agent so Operations Concierge can delegate summarization requests Which action should you perform for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Callable external summarization agent # Add an external agent of type Microsoft Foundry; Correct Foundry project and agent # Enter the Foundry project endpoint URL to create the connection.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The existing specialist should be connected as a Microsoft Foundry agent, not converted into indexed knowledge. A connected agent remains callable and can execute its own summarization and classification behavior when Operations Concierge delegates a matching request. During connection creation, Copilot Studio uses the Microsoft Foundry project endpoint URL to identify the project context and make the relevant agent available for selection. This is distinct from a model endpoint used directly by a custom prompt and from a knowledge- source connection used only for retrieval. Publishing the Copilot Studio agent is part of making a completed design available to users, but it is not the action that establishes the Foundry connection. Adding the Foundry output to an index would turn prior output into retrievable content and would not invoke the specialist on demand. The connected agent should receive a precise description covering long-form transcript summarization and classification, and the integration should be tested with representative inputs to confirm that delegation occurs, the correct Foundry agent runs, and the returned result is incorporated into the main conversation. Study Guide alignment: Integrate and extend agents in Copilot Studio > Configure multi-agent collaboration from Copilot Studio > Integrate a Foundry agent.
NEW QUESTION # 45
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