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| Section | Weight | Objectives |
|---|---|---|
| Deploy AI-powered business solutions | 40–45% | - Monitor, analyze, and tune solutions
|
| Design AI-powered business solutions | 25–30% | - Design extensibility and integration
|
| Plan AI-powered business solutions | 25–30% | - Analyze requirements for AI-powered business solutions
|
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NEW QUESTION # 24
A financial services company uses Microsoft Dynamics 365 Finance.
Currently, the company's support staff manually reviews customer transaction histories to detect potential fraud cases before escalating the cases.
You need to recommend an automation solution for the review process. The solution must ensure that escalations reach a human analyst for final decision making. What should you recommend?
Answer: C
Explanation:
To automate the fraud review process in Microsoft Dynamics 365 Finance while ensuring a human analyst makes the final decision, you should configure the Dynamics 365 Fraud Protection (or integrated Copilot AI agents) to generate risk scores and route high-risk transactions to a manual review queue.
Here is the configuration approach to achieve this:
1. Implement AI-Driven Risk Scoring: Utilize Dynamics 365 Fraud Protection, which uses AI to analyze customer transaction history and assign a risk score (0-999) to each transaction.
2. Define Rules for Escalation: Set up fraud rules in the system to determine which transactions require human intervention. For instance, define a threshold (e.g., a "Minimum score value") where transactions with high fraud probability are automatically flagged.
3. Establish Manual Review Queues: Configure the Manual Review tool to create queues for suspected fraudulent transactions, allowing human analysts to review the AI-generated risk score and transaction history, such as customer behavior, for final, informed decision-making.
4. Use Copilot/AI Agents for Monitoring: Enable AI agents to continuously monitor financial data, such as invoice, payment, and vendor data, and generate alerts for unusual patterns before escalating.
This setup, particularly through the Manual Review workspace, allows for an automated, intelligent, and scalable approach to fraud management.
Reference:
https://www.microsoft.com/en-us/dynamics-365/blog/it-professional/2021/03/25/enhance-your- fraud-workflow-efficiency-with-manual-review/
NEW QUESTION # 25
A company uses Microsoft Foundry agents.
You need to ensure that an agent can dynamically use external tools at runtime without updating the agent.
What should you include in the solution?
Answer: A
Explanation:
The correct answer is A. a Model Context Protocol (MCP) server .
In Microsoft Foundry agent scenarios, MCP is used to let an agent discover and use external tools dynamically at runtime without requiring the agent itself to be modified each time a new tool or capability is added.
Why this is correct:
* MCP standardizes how tools are exposed to agents
* It allows the agent to connect to external capabilities in a flexible, runtime-driven way
* New tools can be made available through the MCP server without rebuilding or updating the core agent definition This directly matches the requirement:
"ensure that an agent can dynamically use external tools at runtime without updating the agent." Why the other options are not correct:
* B. a Microsoft Foundry hub A hub is used more for organizing and managing AI resources/projects, not specifically for dynamic runtime tool exposure.
* C. Microsoft Copilot Studio Copilot Studio is for building conversational agents and workflows, but the question is specifically about dynamic external tool use in Foundry agents .
* D. Azure AI Search Azure AI Search is for indexing and retrieving knowledge, not for dynamically exposing executable external tools.
NEW QUESTION # 26
Scenario: 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? [Select Yes or No]
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 # 27
You are designing a testing solution for Microsoft Copilot Studio agents.
You need to validate prompt engineering best practices to ensure that the agents generate accurate and contextually relevant responses. Which prompt validation techniques and metrics should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To validate prompt engineering f or Microsoft Copilot Studio agents , the goal is to confirm that the agent responds correctly even when users ask the same thing in different ways, and to measure whether the responses are actually useful and correct.
For the prompt validation technique , the correct choice is Use prompts that have varied phrasing . This is a core best practice because real users do not ask questions in one fixed form. They may use different wording, sentence structure, synonyms, or levels of detail. Testing with varied phra sing checks whether the prompt design is robust and whether the agent can still produce the right response across natural language variation.
For the metric , the correct choice is Response relevance and accuracy . Since the requirement is to ensure responses are accurate and contextually relevant , this is the most appropriate measure. It directly evaluates whether the output answers the user's need correctly and in the right context.
Why the other options are not correct:
* Exclude domain-specific term inology from the prompts is not a best practice in business AI solutions.
In many enterprise scenarios, domain-specific terms are essential for accuracy.
* Use only simple, one-word prompts does not reflect real-world usage and would weaken testing coverage.
* The number of words generated per response does not tell you whether the response is correct or contextually appropriate.
* The response generation time is a performance metric, not the best metric for validating prompt quality.
NEW QUESTION # 28
A company uses Azure OpenAI models that use grounding data from Microsoft Fabric for agents. The models are fine-tuned by using proprietary datasets.
You need to design a governance solution that meets the following requirements:
Restricts access to the grounding data to only assigned roles
Restricts model fine-tuning to only the AI engineering team
What 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:
Restricts access to grounding data # Microsoft Purview access policies; Restricts model fine-tuning # Role- based access control (RBAC) in Microsoft Foundry Why Microsoft Purview access policies is correct The grounding data is stored in Microsoft Fabric , and the requirement is to restrict access to that data to only assigned roles.
That is a data governance and access control requirement. Microsoft Purview access policies are the best fit because they are designed to govern and control access to data across enterprise data estates. In this case, they help ensure that only authorized roles can access the grounding data used by the agents.
From an AI business solutions perspective, grounding data is often one of the most sensitive parts of the solution because it can contain:
* proprietary business knowledge
* internal documents
* regulated operational information
* contextual data used to shape model outputs
Purview helps enforce governed access to that data layer rather than relying only on general infrastructure controls.
Why RBAC in Microsoft Foundry is correct
The second requirement is to ensure that only the AI engineering team can perform model fine-tuning .
That is an action-level platform permission requirement. The best control for that is role-based access control (RBAC) in Microsoft Foundry .
RBAC allows the organization to assign permissions based on job function, so only authorized users or groups can:
* create or modify fine-tuning jobs
* manage model assets
* update training configurations
* control deployment-related AI resources
This is the right governance pattern because fine-tuning changes model behavior and can introduce:
* security risk
* compliance risk
* quality drift
* misuse of proprietary datasets
Restricting that capability to the AI engineering team through RBAC creates a clear separation of duties.
Why the other options are incorrect
Azure AI Content Safety
This is used to detect and filter harmful content. It does not control access to Fabric grounding data.
Azure Monitor alerts
Alerts help observe activity, but they do not enforce role-based access to data.
Azure Policy compliance rules
Azure Policy is useful for enforcing resource configuration standards, but it is not the best answer for role- based access to Fabric grounding data or for limiting fine-tuning actions to a specific team.
Azure Resource Manager (ARM) resource locks
Resource locks help prevent deletion or modification of Azure resources, but they do not provide the right permission model for controlling who can perform model fine-tuning operations.
Microsoft Entra Conditional Access
Conditional Access is mainly about sign-in and access conditions, such as device, location, or risk context. It is not the best direct control for restricting fine-tuning permissions inside Foundry.
Expert reasoning
Use this exam shortcut:
* Need to control access to enterprise data # think Purview access policies
* Need to restrict who can perform AI platform actions like fine-tuning # think RBAC in the AI platform So the correct mapping is:
* Restricts access to the grounding data: Microsoft Purview access policies
* Restricts model fine-tuning: Role-based access control (RBAC) in Microsoft Foundry
NEW QUESTION # 29
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