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| Section | Objectives |
|---|---|
| Plan and Design Copilot Studio AI Agents | - Requirements analysis for AI agent solutions - Designing conversation flows and agent behavior - Selecting Copilot Studio capabilities and architecture approach |
| Deploy, Monitor, and Optimize AI Agents | - Monitoring performance and analytics - Iterative improvement and lifecycle management - Publishing and deploying copilots |
| Build and Configure AI Agents in Copilot Studio | - Using generative AI and prompt engineering in Copilot Studio - Creating and configuring copilots and topics - Configuring actions, plugins, and connectors |
| Governance, Security, and Compliance | - Managing access control and authentication - Environment management in Power Platform - Data protection and responsible AI practices |
| Integrate External Systems and Data Sources | - Using Power Automate for workflow orchestration - Integrating Dataverse and Power Platform components - Connecting Azure services and APIs |
>> Exam Microsoft AB-620 Questions <<
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NEW QUESTION # 122
An agent calls a flow to a database. The agent requires structured output values to be returned. The agent receives unexpected or empty values.
You need to configure the agent so that data is exchanged correctly between the agent and the flow. What should you do?
Answer: E
Explanation:
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The first configuration check is the contract between the agent and the flow. Input and output parameter definitions must agree on name, data type, required status, and mapping. A flow can complete successfully yet return an empty value to the agent if the response field is not mapped to the declared output or if the agent expects a different type. Validating the parameter definitions therefore addresses the actual data-exchange requirement. Reviewing run history is useful for diagnosis, but it does not correct a mismatched contract by itself. Changing the trigger schema or adding Parse JSON might be necessary in a specific implementation, yet neither should be done before confirming what the caller sends and what the flow returns. Republishing cannot repair an incorrect mapping. The builder should test the flow directly with known values, inspect each action output, verify the final response action, and then test from the agent. Optional and null fields should be handled explicitly, and sensitive outputs should be minimized and protected in logs. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Add input and output parameters.
NEW QUESTION # 123
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.
Answer:
Explanation:
Explanation:
Repeated failures # Evidence of a recurring pattern; Response-quality information # The response partially matches the expected answer; Evaluation limitation # The specific underlying cause cannot be determined from the score alone.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Three failures on the same fixed interaction, with scores tightly clustered between 0.58 and 0.61, provide evidence of a repeatable problem rather than an isolated stochastic miss. The evaluator ' s reasoning directly supports the conclusion that the response only partially represents the expected answer. The run also shows that internal documentation was retrieved and no tools were involved, which narrows the diagnostic path toward the knowledge, instructions, retrieval quality, or expected-answer design. It does not, however, prove a single root cause. A score and short reasoning statement cannot tell whether the source document is incomplete, the wrong passage ranked highly, the question is ambiguous, the model omitted a key condition, or the expected response is too strict. The next step is to inspect the test transcript, retrieved sources, activity map, and expected answer, then change one factor and rerun the same case. This disciplined triage distinguishes what the evaluation demonstrates from what still requires investigation and prevents an unsupported causal conclusion. Study Guide alignment: Test and manage agents > Evaluate agent performance > Review test results.
NEW QUESTION # 124
A company wants to use content from a non-Microsoft enterprise knowledge base to ground agent responses.
To meet the business needs, the knowledge source must meet the following requirements:
Support grounding from indexed data.
Use existing access controls from the source system.
Support read-only operations.
You need to determine the correct behavior to meet the requirements.
Which behavior meets each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 125
You need to determine which authentication model should be applied to each Blue Yonder Copilot interaction scenario to comply with the company's security and governance requirements.
Which authentication requirement should you apply to each scenario? To answer, move the appropriate authentication requirements to the correct user scenarios. You may use each authentication requirement 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:
NEW QUESTION # 126
Hotspot Question
A company is deploying an agent that uses generative responses. During pilot testing, some responses are blocked by responsible AI filtering. The company has Application Insights enabled.
You need to enable the following:
- Must implement safeguards to block unsafe or harmful content.
- Must implement protections for user prompts and model-generated
responses.
- Must be able to investigate why responses are filtered.
You need to configure Responsible AI safeguards and diagnostics.
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: Configure the content filtering level for AI-generated content.
To configure safeguards and manage blocked generative responses effectively during your pilot testing, you should perform the following action: Configure a custom Azure OpenAI Content Filter policy with stricter severity thresholds and apply it to your agent's deployment.
Box 2: Review Azure Application Insights telemetry
Reviewing Azure Application Insights telemetry is the correct and recommended action to take.
When you deploy generative AI models in Azure (such as Azure OpenAI), safety filters monitor both incoming prompts and outgoing completions. If a response is blocked, Azure logs this event as a specific exception or telemetry metric.
Box 3: Review conversation transcripts
A good action to take is to review conversation transcripts.
Troubleshooting Blocked Responses
Conversation transcripts: Inspecting the downloaded or logged session transcripts allows you to look at the exact turn, user message, or grounded data that caused the Responsible AI filter to trigger the block.
Application Insights telemetry: Correlating the Conversation ID from the error with custom events or exceptions in Azure Application Insights helps verify the exact internal code (such as openAIndirectAttack or ContentFiltered).
Reference:
https://learn.microsoft.com/en-us/troubleshoot/power-platform/copilot-studio/generative-answers/agent-response-filtered-by-responsible-ai
NEW QUESTION # 127
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