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| Section | Weight | Objectives |
|---|---|---|
| Design Agentic AI Solutions | 30-35% | - Identify business scenarios for agentic AI - Define agent capabilities and boundaries - Design multi-agent architectures - Select appropriate AI models and services |
| Governance and Best Practices | 10-15% | - Implement data privacy and security controls - Ensure regulatory compliance - Apply responsible AI principles |
| Deploy and Monitor Agentic AI Solutions | 20-25% | - Optimize agent performance - Publish and deploy agent solutions - Implement error handling and logging - Configure monitoring and analytics |
| Implement Agentic AI Solutions | 30-35% | - Set up agent-to-agent communication - Configure AI agents using Microsoft Copilot Studio - Implement data connections and plugins - Configure security and compliance settings |
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NEW QUESTION # 108
A company deploys a Microsoft Copilot Studio agent that integrates with a Microsoft Power Automate desktop flow.
You need to recommend a testing solution that meets the following requirements:
Test cases must validate the most recent changes to the agent before the agent is released.
The flow must be validated as part of the agent ' s orchestration.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Validate most recent changes # Run tests against the latest unpublished version of the agent; Validate the flow as part of orchestration # Add the flow to the agent as a tool Why the first selection is correct The requirement says the company must validate the most recent changes to the agent before the agent is released . That means testing must happen on the newest working version that has not yet been published to production.
So the correct recommendation is:
Run tests against the latest unpublished version of the agent
This is the safest and most appropriate pre-release testing pattern because it allows the team to:
* verify recent changes before users see them
* catch regressions early
* validate orchestration logic in a controlled state
* reduce production risk
From an AI business solutions perspective, pre-release testing is critical for Copilot agents because even small changes in prompts, tools, orchestration, or data connections can affect:
* response quality
* workflow behavior
* escalation paths
* compliance behavior
* user trust
Testing unpublished changes ensures that governance and quality assurance happen before release, not after deployment.
Why the second selection is correct
The question also requires that the Power Automate desktop flow be validated as part of the agent ' s orchestration .
That means the flow must participate directly in the agent's runtime action path. The correct way to do that is:
Add the flow to the agent as a tool
When a flow is added as a tool, the agent can invoke it during its orchestration. This allows test cases to validate not only the conversational layer, but also the actual execution of the flow inside the end-to-end agent behavior.
From an agentic AI design perspective, tools are how agents connect reasoning with action. If the flow is meant to be part of orchestration, it must be exposed to the agent in a way that allows the agent to call it during task execution.
That is the correct enterprise pattern for validating integrated behavior.
NEW QUESTION # 109
A company has a Microsoft Foundry generative Al model.
You need to evaluate the model ' s output to measure the overall quality and coherence of generated responses. The evaluation must use GPT-4o as a judge and return a numeric score for each output.
Which type of metric should you use?
Answer: D
Explanation:
The requirement says the evaluation must:
measure overall quality and coherence of generated responses
use GPT-4o as a judge
return a numeric score for each output
That maps directly to AI quality (AI assisted).
Why D is correct:
AI-assisted quality metrics use a strong model such as GPT-4o to evaluate generated outputs against qualitative dimensions like coherence, relevance, completeness, and overall response quality, and then assign scores.
Why the other options are not correct:
A). Groundedness checks whether the answer is supported by the source/context, not overall quality and coherence.
B). AI quality (NLP) usually refers to traditional NLP-style metrics rather than judge-model scoring with GPT-
4o.
C). risk and safety evaluates harmfulness, toxicity, jailbreak risk, or policy concerns, not overall response quality.
NEW QUESTION # 110
Case Study 2 - Contoso, Ltd
Overview
Contoso, Ltd. is a high-tech manufacturing company that uses Microsoft Dynamics 365 Finance.
Dynamics 365 Supply Chain Management, and Dynamics 365 Commerce for its North American operations. The company designs and develops innovative products that have many patents and proprietary technologies. The patents and engineering designs are closely guarded secrets.
Contoso executives want to integrate and adopt AI solutions to help scale the company in preparation for an anticipated period of rapid growth.
The company has multiple legal entities and Azure subscriptions that will be used in the adopted AI solutions.
Requirements
AI Adoption
The following executives will have specific responsibilities in the overall AI adoption:
- Chief Technology Officer (CTO): Select one Dynamics 365 Finance,
Dynamics 365 Supply Chain Management or Dynamics 365 Commerce prebuilt
AI agent and one custom Microsoft Copilot Studio AI agent to prioritize and deploy during the initial AI adoption phase.
- Chief Information Officer (CIO): Ensure that appropriate security
labels are assigned to the data used by the AI agents.
- Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the AI agents being deployed.
- Chief Information Security Officer (CISO): Discover and inventory AI
resources for auditing.
- Chief Executive Officer (CEO): Ensure that all solutions adhere to
industry-standard responsible AI practices.
All AI initiatives and agents will have a detailed business use case, a defined audience profile, and an estimated ROI that will compare the cost savings of the current process against the estimated costs of using the new AI solutions.
The company's research and development (R&D) department already has a custom Model Context Protocol (MCP) server that contains comprehensive product specifications and compliance data.
Prebuilt AI Agent
The CTO has NOT yet selected which prebuilt AI agent to use in Dynamics 365 Supply Chain Management. The CTO wants to view available agent templates to identify which agent will add the most business value.
Depending on which high-priority AI agents are identified, its agent capabilities must be previewed in a discovery meeting with the relevant business operation stakeholders.
Custom AI Agent
Contoso has identified the following custom AI agent requirements:
- The custom AI agent will use data from Dynamics 365 Supply Chain
Management to answer questions for the manufacturing team as a low-code solution.
- The custom AI agent will be accessible from within Microsoft Teams.
- The custom AI agent must be designed to eventually connect to other
agents that can be selected based on their description.
- The topics used in the custom AI agent will be selected based NOT on
a trigger phrase, but on a description of the purpose of the query, to
make the interactions more conversational.
- The custom AI agent must be able to answer questions about product
specifications by using existing technologies. The product
specifications are maintained by the R&D department.
- The custom AI agent must be integrated with and accessible from
Dynamics 365 Supply Chain Management.
- The custom AI agent must be able to use Dynamics 365 Supply Chain
Management business logic that is stored outside of the application.
Analysis, Reporting, and Troubleshooting
Contoso has identified the following analysis, reporting, and troubleshooting requirements:
- The CISO will audit all the AI solutions monthly for compliance and
security.
- The CFO will analyze all the AI solutions quarterly to compare the
estimated ROI against actual measured efficiencies and adoption. The
CFO will use the Copilot Studio agent usage estimator to perform this
analysis.
- The CISO wants to identify how much sensitive data was accessed for a given AI agent run and who accessed the data. Too much sensitive data accessed by a single user might indicate a high security risk.
- The CTO wants to track user feedback on the quality of the AI agent
responses during user interactions with the agents. Consistently poor
feedback will trigger an escalated reengineering discussion.
- The CEO wants a quarterly assessment of all the required metrics for
their specific responsibilities. The tools used for the assessments
must be Microsoft-recommended and must verify reliability,
interpretability, fairness, and compliance.
- The CFO wants to identify how many interactions with the AI agents
are abandoned on a given day as compared to resolved conversations. Too many abandoned sessions might indicate that Copilot Studio credits are being used inefficiently by end users.
What should you recommend to assist the CEO with their specific responsibilities?
Answer: A
Explanation:
Scenario: The CEO wants a quarterly assessment of all the required metrics for their specific responsibilities. The tools used for the assessments must be Microsoft-recommended and must verify reliability, interpretability, fairness, and compliance.
To ensure your Microsoft Dynamics 365 AI agent meets quarterly compliance and ethical standards, the Responsible AI (RAI) Dashboard in Azure Machine Learning is the primary recommended tool.
Microsoft recommends aligning quarterly assessments with their Responsible AI Standard v2, which focuses on six core principles: fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard
NEW QUESTION # 111
A company has Microsoft 365 Copilot agents.
You need to design a security solution for the agents. The solution must meet the following requirements:
Identify and mitigate potential risks that relate to AI use.
Protect AI apps and the sensitive data processed or generated by the agents.
Support responsible AI governance by retaining and logging interactions, detecting policy violations, and investigating incidents.
Which two components should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are A. Microsoft Purview and D. Microsoft Defender .
This question is asking for an enterprise security and governance design for Microsoft 365 Copilot agents
. The requirements span three major control areas:
* identify and mitigate AI-related risks
* protect AI apps and sensitive data
* retain/log interactions, detect policy violations, and investigate incidents No single tool in the list fully covers all of those needs. The best solution is the combination of Microsoft Purview and Microsoft Defender .
Why A. Microsoft Purview is correct
Microsoft Purview is the strongest match for the requirements around:
* protecting sensitive data
* governance of AI usage
* retaining and logging interactions
* detecting policy violations
* supporting investigation and compliance processes
Purview is central to Microsoft's information protection, compliance, insider risk, auditing, and data governance capabilities. In the context of Microsoft 365 Copilot agents, Purview helps organizations:
* classify and label sensitive data
* apply data loss prevention controls
* retain records and interactions
* audit activity
* investigate policy issues
* support responsible AI governance practices
From an AI business solutions perspective, this is essential because copilots often process sensitive enterprise information, and organizations need visibility into how that information is used, exposed, and governed.
Why D. Microsoft Defender is correct
Microsoft Defender addresses the requirement to identify and mitigate potential risks that relate to AI use and to protect AI apps .
Defender is the broader security layer that helps monitor and protect applications, detect threats, identify vulnerabilities, and support incident response. In AI-enabled enterprise solutions, Defender helps secure the application environment and detect risk patterns that could affect AI systems or the data they use.
This is important because AI security is not only about content and compliance. It is also about:
* threat detection
* app protection
* attack surface awareness
* suspicious activity monitoring
* incident investigation
Defender complements Purview by focusing more on the security posture and threat protection side of the solution.
Why the other options are incorrect
B). Azure AI Content Safety
Azure AI Content Safety is valuable for filtering harmful or unsafe AI-generated or user-supplied content.
However, it does not fully address the broader requirements here around enterprise data protection, interaction retention, policy logging, governance, and incident investigation. It is useful, but not the best two-part answer.
C). role-based access control (RBAC) in Microsoft Foundry
RBAC is important for access management, but this option is too narrow and also not the best fit for Microsoft 365 Copilot agents in this question. It does not cover the required governance, retention, policy violation detection, or investigation capabilities.
Expert reasoning
A good way to solve this kind of question is to separate the requirements into two control domains:
* data governance, retention, policy, compliance # Microsoft Purview
* threat protection, risk mitigation, app security, investigation support # Microsoft Defender That pairing gives the most complete answer across the listed options.
NEW QUESTION # 112
A company has an Al agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
Answer: C
Explanation:
The requirement is to make sure the data ingested by the agent is clean and suitable for the intended use, which is producing monthly sentiment insights to guide product development and marketing.
The best answer is C. Identify and address biased data.
Why C is correct:
For sentiment analysis and reporting, biased data can distort conclusions and produce misleading recommendations Data preparation should include checking for skew, unfair representation, missing segments, and other quality issues that affect downstream decisions This aligns with responsible AI and sound analytics practice
NEW QUESTION # 113
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