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| Section | Weight | Objectives |
|---|---|---|
| Plan AI-powered business solutions | 25–30% | - Evaluate costs and benefits
|
| Design AI-powered business solutions | 25–30% | - Orchestrate configuration
|
| Deploy AI-powered business solutions | 40–45% | - Manage testing and validation
|
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NEW QUESTION # 61
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.
Drag and Drop Question
Which tools should you recommend to assist the CISO and the CIO with their specific responsibilities? To answer, drag the appropriate tools to the correct executives. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 62
You are evaluating a Microsoft Copilot Studio agent that supports Microsoft Dynamics 365 Customer Service representatives.
You need to recommend a testing solution that meets the following requirements:
Evaluates agent effectiveness during active sessions
Validates whether the agent delivers accurate and helpful responses
Provides measurable, actionable insights for continuous improvement
What should you recommend?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is A. Track resolution, deflection, and accuracy by using dashboards and use scripts to ensure consistent responses.
This question is about evaluating a Copilot Studio agent in live support operations, not just testing technical uptime or infrastructure performance. The requirements emphasize three things:
effectiveness during active sessions
response accuracy and helpfulness
measurable insights for continuous improvement
That combination points to operational quality metrics and analytics dashboards.
Why A is correct
Tracking resolution, deflection, and accuracy directly measures how well the agent performs in real support conversations:
Resolution shows whether the issue is successfully handled
Deflection shows whether the agent reduces human workload appropriately Accuracy shows whether responses are correct and helpful Using dashboards gives leaders and support teams measurable, ongoing visibility into agent behavior. Adding scripts for consistent testing further supports repeatable evaluation and improvement.
From an AI business solutions perspective, this is the right recommendation because it combines:
business outcome measurement
quality validation
operational analytics
continuous improvement feedback loops
This is exactly how enterprise copilots should be managed after deployment.
Why the other options are incorrect
B). Perform load testing to validate how the agent scales under a high chat volume Load testing is useful for scalability and capacity planning, but it does not directly validate whether responses are accurate, helpful, or effective during active sessions from a business-outcome perspective.
C). Review historical tickets to find agents that have the shortest resolution times This may give some retrospective insight, but it does not directly evaluate the Copilot Studio agent during active sessions, and shortest resolution time alone does not prove response quality or helpfulness.
D). Measure uptime and page load times
These are infrastructure and availability metrics. They are important for system health, but they do not evaluate conversational effectiveness or answer quality.
Expert reasoning
For Copilot evaluation questions:
if the goal is business effectiveness in active sessions, use resolution/deflection/accuracy if the goal is system scale, use load testing if the goal is infrastructure reliability, use uptime and latency
NEW QUESTION # 63
You are designing end-to-end test scenarios for a business solution that uses Microsoft Dynamics 365 Sales and Dynamics 365 Finance. You need to ensure that the business solution meets the following test requirements:
* Properly exchanges data between the Dynamics 365 apps
* Aligns with defined user workflows and business processes
Which type of testing should you use for each requirement? To answer, drag the appropriate testing types to the correct requirements. Each testing type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For end-to-end validation of a solution that uses Dynamics 365 Sales and Dynamics 365 Finance , the testing type should match the goal of each requirement.
For properly exchanging data between the Dynamics 365 apps , the correct testing type is Integrat ion testing.
This verifies that the systems connect correctly, pass data accurately, and maintain consistency across app boundaries.
For aligning with defined user workflows and business processes , the correct testing type is User acceptance testing. This focuses on whether the solution supports real business tasks the way users expect and whether it fits the intended operational process.
Why the other options are not correct here:
* Drift is about changes over time, often in model or behavior consistency.
* Exploratory is useful for uncovering unexpected issues, but it is not the primary match for the stated requirement.
* Performance focuses on speed, scale, and responsiveness, not workflow fit.
NEW QUESTION # 64
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
Your organization creates a new AI Center of Excellence (CoE) to guide enterprise-wide adoption of generative AI. A project team submits a proposal requesting immediate development of a generative AI model. They argue that identifying use cases and validating data quality can wait until after the prototype is built, since the CoE can "fix the data later." You are asked whether this approach aligns with Microsoft's recommended AI adoption lifecycle, which starts with identifying use cases, selecting domain-specific data, preparing and validating that data, designing and training solutions, and then monitoring and adapting them over time.
According to Microsoft's AI adoption guidance, is it appropriate to skip identifying use cases and validating domain-specific data before beginning AI model development?
Answer: B
Explanation:
Microsoft's generative AI adoption framework - as shown in the diagram - emphasizes a sequenced lifecycle:
Identify use cases
Prepare, validate, and aggregate the required data
Design, train, and validate AI solutions
Monitor and adapt
The Microsoft Learn module clearly states that a Center of Excellence ensures organizations start with aligned business use cases and validated domain-specific data before any model development begins.
Skipping these early steps introduces high risk, creates misaligned solutions, and prevents effective contextualization of AI models.
Therefore, beginning model development without first identifying use cases and validating data does not follow Microsoft's recommended AI planning and adoption process.
References:
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/2-how-center- excellence-assists-planning-adoption-generative-ai
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/1-introduction- generative-ai-center-excellence
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/center-of- excellence
NEW QUESTION # 65
A company has multiple AI models that support generation of sales transactions.
Each release of the models must be reviewed by a security and compliance team before being deployed to the production environment. The security and compliance team must have access to prior versions to properly determine potential exposures introduced.
You need to recommend a solution to evaluate the impact of each deployment to production. The solution must enhance business continuity.
What should you recommend?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is C. Implement version control for all the AI system components .
This question is not only about model approval. It is about creating a deployment process that allows the organization to:
* review every release before production
* compare current and prior versions
* evaluate the impact of changes
* improve business continuity if a deployment introduces risk
That makes version control for all AI system components the strongest answer.
Why C is correct
The requirement says the security and compliance team must have access to prior versions to determine exposures introduced by each release. That means the organization must be able to track, compare, and potentially roll back not just the model itself, but the broader AI solution over time.
In real enterprise AI deployments, "AI system components" usually include:
* models
* prompts
* orchestration logic
* configuration files
* policies
* connectors
* inference code
* evaluation assets
* deployment definitions
If only the model is versioned, the team may miss exposure introduced by surrounding components. For example:
* a prompt change could create unsafe outputs
* a policy/configuration change could expose sensitive data
* an orchestration update could alter transaction behavior
* a connector change could affect compliance boundaries
That is why full AI system version control is the best answer. It gives security and compliance teams complete visibility into what changed across releases.
It also enhances business continuity because version control supports:
* rollback to known-good versions
* change auditing
* release comparison
* traceability
* controlled recovery from faulty deployments
From an agentic AI business solutions perspective, this is the most robust governance pattern because AI outcomes are rarely determined by the model alone. They are determined by the entire solution stack.
Why the other options are less appropriate
A). Create a central model registry that uses version history
A model registry is useful, and version history helps, but this option is too narrow. The question asks about evaluating the impact of each deployment and enhancing business continuity. In enterprise AI systems, impact is often caused by more than just the model artifact. A model registry does not necessarily capture all surrounding components that affect production behavior.
B). Establish a promotion process by using a quality gate
A quality gate is valuable for approval workflows, but it does not by itself satisfy the need for deep access to prior versions across the system. It controls promotion, but it does not fully provide historical traceability and rollback coverage for all AI system components.
D). Track model retirement schedules to prevent service disruptions
This may support lifecycle planning, but it does not address the core requirement of comparing releases, reviewing prior versions, and evaluating exposure introduced by each deployment.
Expert reasoning
This question combines three ideas:
* security/compliance review
* access to prior versions
* business continuity
When those appear together, the strongest answer is typically the one that provides end- to-end traceability and rollback across the whole solution , not just a single artifact.
That is why version control for all AI system components is the best recommendation.
So the correct choice is:
A nswer: C
NEW QUESTION # 66
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