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| Section | Weight | Objectives |
|---|---|---|
| Plan and Configure Agent Solutions | 30-35% | - Plan agent solutions
|
| Test and Manage Agents | 20-25% | - Monitor and manage agent solutions
|
| Integrate and Extend Agents in Copilot Studio | 40-45% | - Integrate agents with enterprise systems
|
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NEW QUESTION # 92
Drag and Drop Question
A development team is reusing agents in Copilot Studio across development, test, and production environments.
You need to ensure the agents meet the following requirements:
- Reusable components must support governed promotion across
environments.
- Environment-specific configuration must not be hard-coded.
- Deployments must support automated, multi-stage approvals.
You need to configure the agents based on the business requirements.
Which configurations should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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: Export as a component collection
Component Collections can be used to implement reusable components that support governed promotion across development, test, and production environments.
In Microsoft Copilot Studio, Component Collections act as shared containers that package together topics, actions, entities, or knowledge sources. Because these collections are stored natively within Microsoft Power Platform solutions, they natively support Application Lifecycle Management (ALM) pipelines for secure, governed environment deployment.
Box 2: Use connection references and environment variables
Box 3: Use an ALM pipeline
Power Platform pipelines natively support gated approval extensions. You can implement multi- stage approval workflows that halt a deployment request until your required business rules, automated validation, or stakeholders authorize the promotion to the next stage.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-export-import-copilot-components
NEW QUESTION # 93
Drag and Drop Question
A company uses an agent in Copilot Studio to answer questions based on enterprise content.
The content is indexed in Azure AI Search. Responses must be generated by a Microsoft Foundry model.
To meet the business needs, the solution must meet the following requirements:
- Users are able to trace each answer back to its source.
- The agent retrieves information from a centralized data index.
- Responses are generated by an enterprise-approved foundation model.
You need to configure generative answers for the agent.
What should you configure for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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: Instruct the agent to cite sources in knowledge settings
The specific behavior of how the agent presents that information-such as enforcing citations and tracing links back to the source documents-is controlled by the instructions and configurations within the agent's knowledge and conversation settings. You must explicitly configure the agent's prompt instructions or system settings to format the output with source citations so users can trace the answers.
Box 2: Configure Azure AI Search as the grounding data source
By adding Azure AI Search as a knowledge source Microsoft Copilot Studio enables the Retrieval-Augmented Generation (RAG) pattern. This ensures your generative AI model retrieves specific enterprise content from your centralized index and uses it to ground responses.
Box 3: Deploy a model in Foundry to an agent and connect to Copilot Studio Model Control: To strictly guarantee that an enterprise-approved foundation model (such as a specific Microsoft Foundry model) generates the response, you must manually deploy that specific model and link it to your agent.
Connection Workflow: Deploying the custom model in Azure AI Studio (formerly encompassing parts of Azure AI Foundry) and connecting it to Copilot Studio ensures the orchestration layer routes prompts directly to your certified LLM.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 94
An agent in Copilot Studio is evaluated using a fixed test set and an automated evaluation method.
After running the evaluation, the team observes that the same three interactions fail in every run, while all other interactions pass consistently.
You need to use the evaluation results to determine an accurate conclusion.
What should you conclude from the evaluation results?
Answer: C
Explanation:
This result means the outcomes successfully isolate which portions of the agent's functional scope require remediation.
When an automated evaluation yields perfectly consistent results-where the same three interactions fail every time and all others pass-it indicates that the evaluation environment is stable and free of random noise.
High Determinism: The agent behaves predictably under test conditions.
Eliminates Flakiness: You can rule out intermittent network errors or transient system glitches.
Specific Scope: The failures point directly to specific broken intents, missing data, or faulty logic pathways.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-agent-evaluation-results
NEW QUESTION # 95
A company is building an agent in Copilot Studio.
The agent must meet the following requirements:
Responses must be grounded in approved internal content.
The agent must be able to retrieve information from configured data sources in a topic.
You need to configure the agent so that topic responses are grounded from custom data sources.
What should you configure?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A generative answers node inside the topic is the correct place to retrieve from configured knowledge sources for that specific conversational path. The node can use the user's message as input, search designated sources, and generate a grounded response from the retrieved content. Topic-level sources can override the broader agent-level set, which is useful when the topic must restrict itself to approved data. A custom prompt that merely instructs the model to use company information does not create or enforce a retrieval connection. Increasing the response limit affects length, not grounding. Analytics measures usage after the fact and cannot supply knowledge. Adding SharePoint only at agent level is unnecessarily narrow and would not satisfy the requirement for custom sources configured within the topic. The builder should enable Search only selected sources when strict scoping is required and turn off general knowledge if no ungrounded fallback is allowed. Testing should inspect both the answer and the resource details to confirm that the intended source supplied the evidence. Study Guide alignment: Plan and configure agent solutions > Configure topics > Configure generative answers node; Configure advanced agent responses with custom knowledge sources.
NEW QUESTION # 96
An agent must escalate to a live human agent when it cannot resolve a customer issue, and hand off full conversation context. Which node should you add to the topic?
Answer: A
NEW QUESTION # 97
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