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질문 # 73
You need to design a shared prompt library that will be used across multiple business units. The solution must meet the following requirements:
Ensure consistent AI responses with reusable formats.
Support governance and version control.
Minimize administrative effort.
Minimize ongoing costs.
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.
정답：
설명：
Explanation:
Ensure consistent AI responses # Define standardized prompt templates; Support governance and version control # Store prompts in a Git repository The requirement says the shared prompt library must ensure consistent AI responses with reusable formats.
The best way to achieve that is to create standardized prompt templates.
Standardized templates help multiple business units use the same structure for:
system instructions
response style
output formatting
safety guidance
business terminology
escalation behavior
This is important in AI-powered business solutions because prompt variability often leads to inconsistent outputs. A shared template approach creates repeatability and reduces drift across departments.
From an enterprise AI design perspective, standardized prompt templates improve:
response consistency
quality control
reusability
onboarding speed
compliance alignment
They also minimize administrative effort because teams do not need to invent new prompt structures from scratch.
Why "Store prompts in a Git repository" is correct
The requirement says the solution must support governance and version control while minimizing effort and cost.
A Git repository is the most appropriate answer because it provides:
version history
change tracking
rollback capability
collaboration support
approval workflows
auditability
This directly supports governance. It allows the organization to know:
who changed a prompt
what changed
when it changed
why it changed
That is extremely valuable for enterprise AI operations, especially when prompts influence business decisions, customer interactions, compliance behavior, or internal process automation.
Git also minimizes ongoing cost because it uses standard development and content management practices rather than requiring a separate expensive governance platform for prompt assets.
Why the other options are incorrect
Delegate department-specific prompt templates
This reduces consistency instead of improving it. Different departments creating their own templates introduces fragmentation, style drift, and governance challenges.
Maintain a prompt history
Keeping a history is useful, but it does not by itself ensure consistent reusable formats. It is more of a recordkeeping activity than a standardization mechanism.
Categorize prompts by business function
Categorization is helpful for organization and discoverability, but it does not provide version control or governance by itself.
Define standardized prompt templates for governance/version control
Standardization improves consistency, but version control specifically requires a system for tracking and managing changes. That is why Git is the better answer for the governance requirement.
Expert reasoning
A shared enterprise prompt library should usually follow this pattern:
Consistency and reuse # standard templates
Governance and versioning # Git-based repository
Low cost and low admin overhead # reuse existing tooling and centralized controls
질문 # 74
You are creating validation criteria for a custom generative Al model that produces business reports based on internal enterprise data. You need to Assess whether the model ' s outputs are appropriate and meaningful (or the business reports. Which metric should you use?
정답：B
설명：
The correct evaluation criterion is alignment of the output to domain-specific tasks . The objective is to establish whether generated reports are semantically appropriate, useful, and correct for the intended business purpose. This requires evaluating output quality against the requirements and expectations of the specific business domain.
Microsoft ' s generative-AI evaluation guidance emphasizes that evaluation should reflect the nature of the task and dataset , and that domain-specific scenarios frequently require customized evaluation criteria.
Training duration and inference resource utilization are operational or performance measurements, not measures of whether the generated report is meaningful. Likewise, active-user count measures adoption rather than model-output quality.
질문 # 75
What should you include in the custom Al agent design to meet the R & D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
To expose the data to the agent, create: a custom connector
Add to the agent: the MCP server
The custom agent must answer questions about product specifications and compliance information, and the case study states that the R & D department already has a custom Model Context Protocol (MCP) server containing that information.
The best design is:
create a custom connector to expose that external capability cleanly to the low-code Copilot solution add the MCP server to the agent so the agent can use that external knowledge/tooling source Why this is correct:
The MCP server already exists and contains the needed product-specification and compliance data.
In a Copilot/agent design, you need a way to expose external functionality and data in a reusable, secure way.
A custom connector is the low-code integration mechanism that fits this requirement.
Then the agent can use the MCP server as the connected external capability for answering those questions.
Why the other options are not correct:
Azure AI Bot Service channel is for communication channels, not for exposing this knowledge source.
a custom OData entity is not the right pattern for integrating the existing MCP-based capability.
the Semantic Kernel is a developer orchestration framework, but the requirement emphasizes using the existing MCP technology in a low-code solution.
an event trigger is unrelated to exposing R & D specification/compliance knowledge.
a REST API is too generic here; the scenario specifically points to the existing MCP server.
a tool is close conceptually, but the most direct answer choice tied to the existing R & D technology is the MCP server.
질문 # 76
A company has a Microsoft Power Platform environment.
You need to build two agents named Agent1 and Agent2. The solution must meet the following requirements:
* Agent1 must be extendable by using the Semantic Kernel and must connect to multiple business apps and APIs.
* Agent2 must connect directly to data stored in Microsoft Dataverse and must be embeddable in a Microsoft Power Apps canvas a pp.
What should you use to build each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Verified Answer : =
* Agent1 # Microsoft Foundry
* Agent2 # Copilot in Power Apps
Comprehensive and Detailed Explanation from Agentic AI Topics:
For Agent1 , the requirement is that it must be extendable by using Semantic Kernel and connect to multiple business apps and APIs . The best fit is Microsoft Foundry because Foundry-based agents are designed for extensibility and developer-oriented orchestration, including integration patterns that work well with Semantic Kernel and external tools/APIs.
For Agent2 , the requirement is that it must connect directly to Microsoft Dataverse and be embeddable in a Power Apps canvas app . The best fit is Copilot in Power Apps , because it is designed for Power Platform-native experiences, works naturally with Dataverse-backed app data , and is intended for embedding AI experiences inside canvas apps .
Why the other options are not the best match:
* Azure Logic Apps is for workflow orchestration, not the primary platform for building these agents.
* Microsoft Copilot Studio is strong for conversational agents, but the wording here points more directly to Power Apps-native embedding for Agent2 and Semantic Kernel extensibility for Agent1.
질문 # 77
You are designing a testing solution for a Microsoft Copilot Studio agent that integrates with Microsoft Dynamics 365 Customer Service and Dynamics 365 Sales.
You need to design end-to-end scenarios to test the agent ' s ability to perform the following actions:
Coordinate tasks and data interactions across both Dynamics 365 apps.
Interpret user input and provide contextually relevant outputs.
Which test scenario and metric should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Test scenario # Run task-based scenarios that involve both apps; Metric # Track the successful completion of cross-app tasks Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is D:
Test scenario # Run task-based scenarios that involve both apps
Metric # Track the successful completion of cross-app tasks
Why this test scenario is correct
The question explicitly asks for an end-to-end testing design for a Copilot Studio agent that integrates with both:
Dynamics 365 Customer Service
Dynamics 365 Sales
The required capabilities are:
coordinating tasks and data interactions across both apps
interpreting user input and returning contextually relevant outputs
That means the testing approach cannot be isolated to one app at a time. It must validate the agent's behavior across the full multi-application business workflow.
That is why the correct test scenario is:
Run task-based scenarios that involve both apps
This kind of test validates whether the agent can successfully move through realistic business processes such as:
reading customer context from one app
updating or retrieving related sales information in the other
maintaining context through the workflow
responding appropriately based on user intent across systems
From an agentic AI business solutions perspective, this is the right design because true enterprise agent validation must focus on workflow execution, not just component-level checks.
Why this metric is correct
The best metric is:
Track the successful completion of cross-app tasks
This is the most direct way to measure whether the agent is actually achieving the intended business outcome across both Dynamics 365 applications.
Why this matters:
The requirement is about coordination across apps
The test is end-to-end
The goal is not just speed or UI consistency
The agent must complete business tasks successfully across systems
A cross-app completion metric shows whether the agent can:
understand the user's request
maintain context
retrieve or update the right information
finish the workflow correctly across app boundaries
This is much more meaningful than measuring clicks or simple response time.
Why the other options are incorrect
A). In each app, test isolated tasks without using workflows / Measure initial prompt response time This fails the end-to-end requirement. Isolated tasks do not validate cross-app orchestration, and response time does not prove successful workflow execution.
B). Run task-based scenarios that involve both apps / Track average click rate across both apps The scenario part is good, but average click rate is not the right success metric for Copilot task orchestration.
Clicks do not reliably measure whether the business process was completed correctly.
C). Test visual consistency across both apps / Track successful completion of cross-app tasks The metric is good, but the test scenario is wrong. Visual consistency is a UI concern, not an end-to-end functional validation of cross-app agent behavior.
Expert reasoning
For exam questions like this:
If the requirement says end-to-end across multiple apps, choose task-based scenarios involving both apps If the goal is business workflow success, choose a metric tied to task completion, not visual design, click rate, or raw response speed
질문 # 78
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