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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy AI-powered business solutions | 45% | - Security, governance, and ALM
|
| Topic 2: Design AI-powered business solutions | 30% | - Extensibility and orchestration
|
| Topic 3: Plan AI-powered business solutions | 25% | - AI solution design strategy
|
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NEW QUESTION # 87
Which framework should you use to meet t he AI agent requirements for the sales cycle enablement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For Microsoft Copilot Studio best practices:
# Microsoft Power Platform Well #Architected Framework
Why?
* Copilot Studio is part of the Power Platform.
* Microsoft's official guidance for building scalable, s ecure, compliant Power Platform solutions is the Power Platform Well #Architected Framework .
* It includes governance, security, reliability, operational excellence, and cost optimization-exactly what Fabrikam needs for AI agent deployment, telemetry, compliance, and ROI.
For conversational user experiences:
# Success by Design
Why?
* Success by Design is Microsoft's methodology for implementing Dynamics 365 and Power Platform solutions.
* It includes conversational design checklists , solution blueprinting, risk assessments, and user #adoption strategies.
* Fabrikam's scenario emphasizes:
* User adoption
* Conversational UX quality
* Escalation paths
* Compliance
* Telemetry
* These are all covered in Success by Design's structured implementation approach.
https://learn.microsoft.com/en-ca/power-platform/well-architected/experience-optimization/conversation-design
Topic 1, Fabrikam, Inc
Background - Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technolog ies used by its business-to-business (B2B) sales team. As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations. Problem Statements - Fabrika m ' s infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes
, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience. Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month.
As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority. Planned Initiatives - General - Fabrikam management has prioritized AI-driven projects to improve efficie ncy, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration - Fabrikam plans to migrate from its cur rent on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users. All the data from the different systems will be consolidated int o a single data source - a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team. Sales Cycle Enablement - To achieve the company ' s objectives, Fabrikam intends to implement the following s trategies to enhance the sales cycle:
Use low-code development to create a single AI agent that has Dataverse as its core component. Ensure that sales managers can access unanswered correspondence from prospects and intervene as appropriate. Replace the pr evious proprietary software with Dynamics 365 Sales to track sales cycles and customer interactions.
Have the sales executives use Dynamics 365 Sales to track interactions for open opportunities and send follow-up communications to prospects. Have the sale s executives use handsfree headsets to interact with an AI agent when they have questions about internal policies or customer data. Requirements - Infrastructure Migration - Fabrikam has identified the following infrastructure migration requirements: Azu re must be used for all future infrastructure workloads. The company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud. Any created AI agents must have their return on investment (ROI) calculated to ensure that the s olution will save the company money. Sales Cycle Enablement - Fabrikam has identified the following requirements for sales cycle enablement: The final AI agent must follow Microsoft recommendations for a conversational user experience. A designated checkl ist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution. Detailed telemetry must be logged for the first created AI agent to help troubleshoot and optimize the agent during the initial AI agent adoption process. Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts. The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off. The sales team must use Dynamics 365 Sales to correspond with prospects more quickly and efficiently than currently. Sales managers must report on the adoption of the AI agent to key Fabrikam stakeholders on a monthly basis. Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
NEW QUESTION # 88
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.
Answer:
Explanation:
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
NEW QUESTION # 89
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
Answer: A
Explanation:
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size Why the other options are less suitable:
A). Chunk documents during indexing can improve retrieval quality in some RAG scenarios, but it does not directly guarantee lower total retrieved token volume in the final prompt B). Lower the maximum token usage limit for the responses may reduce output length, but it does not solve excessive input-context usage and can hurt response quality C). Reduce the number of knowledge sources used by the agent is too blunt and may remove useful grounding unnecessarily
NEW QUESTION # 90
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: B
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 # 91
Hotspot Question
A company has Microsoft Power Platform development, staging, and production environments.
Each environment has its own Microsoft Dataverse tables and Azure AI Search index.
You are designing an application lifecycle management (AIM) process to deploy a Microsoft Copilot Studio agent between the environments.
The company has a Copilot Studio agent named Agent1 in development. Agent1 uses the following grounding data sources:
- A Dataverse table named CustomerOrders
- An Azure AI Search index named customer-knowledge
You need to deploy Agent1 to production. The solution must ensure that the agent uses the production grounding data sources, minimizes downtime, and handles credentials and endpoints securely.
What should you include in the deployment package solution, and what should you reconfigure after the deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 92
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