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| Section | Weight | Objectives |
|---|---|---|
| Design AI-powered business solutions | 25โ30% | - Design AI and agents
|
| Deploy AI-powered business solutions | 40โ45% | - Implement security, governance, compliance
|
| Plan AI-powered business solutions | 25โ30% | - Analyze requirements for AI-powered business solutions
|
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NEW QUESTION # 26
Hotspot Question
A company has a Microsoft 365 E5 subscription and uses Microsoft Copilot Studio.
The company has a Microsoft SharePoint Online library that contains 10,000 policy PDFs from various departments. The library contains a populated column named Department for each PDF.
You need to design a Copilot Studio agent that will use the SharePoint library as a knowledge source. The solution must meet the following requirements:
- Enable the agent to answer user questions about company policies.
- Ensure that the agent can identify which departments and policies are connected.
What should you include in the design for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: From Copilot Studio, add SharePoint as a knowledge source
Enable the agent to answer user questions about company policies.
To enable a Microsoft Copilot Studio agent to answer questions from a large SharePoint Online library using the Department column as a filter, you must configure Generative Answers with specific metadata filtering.
*-> 1. Configure SharePoint as a Knowledge Source
Add the Source: In Copilot Studio, go to the Knowledge tab and select Add knowledge. Choose SharePoint and provide the URL of your site or specific document library.
Authentication: Ensure Manual Authentication with Microsoft Entra ID is configured. The agent requires the Sites.Read.All and Files.Read.All scopes to access documents on behalf of the user.
Indexing: Note that indexing tens of thousands of files can take significant time (days or weeks).
2. Enable Metadata Filtering (Department Column)
To ensure the agent only searches policies relevant to a user's department, you must use Advanced Settings to filter the search results based on your custom column.
Box 2: From Copilot Studio, configure the SharePoint tool.
Ensure that the agent can identify which departments and policies are connected.
The SharePoint tool can be configured directly from Microsoft Copilot Studio to act as a knowledge source, allowing your AI agent to read files, documents, and lists from SharePoint to provide answers.
Note:
To ensure your Copilot Studio agent effectively identifies and connects your "Department" metadata with specific policies across tens of thousands of documents, you must address both search indexing and agent instructions.
To ensure that your Microsoft Copilot Studio agent can identify and filter documents based on the Department column from your SharePoint library, you should configure the Advanced settings of your SharePoint knowledge source to use KQL (Keyword Query Language) filters.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-add-sharepoint
NEW QUESTION # 27
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:
NEW QUESTION # 28
A company has Microsoft Foundry agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
A company mandate states that the agents and their grounding data must adhere to data residency and movement regulations.
You need to recommend a governance solution for the agents.
What should you include in the recommendation?
Answer: D
Explanation:
In this scenario, Microsoft Foundry agents and Azure OpenAI resources generate responses by using the Responses API. To ensure these agents adhere to data residency and movement regulations across the United States and Europe, Microsoft Purview should be included to provide the following governance and security controls:
Unified Data Discovery & Classification: Purview's discovery REST API allows orchestrator agents to identify relevant data assets (e.g., in Fabric or Databricks) across the organization's entire data landscape.
Sensitivity Label Enforcement: It ensures that AI-generated responses respect existing access controls by checking document label metadata at query time. This prevents oversharing of sensitive data and restricts users to authorized content.
Data Loss Prevention (DLP): By integrating Purview DLP policies, organizations can monitor, block, or warn when sensitive data is used in AI prompts or responses in real-time.
Data Residency Compliance: For strict European residency (e.g., GDPR), Azure OpenAI resources should be deployed using Data Zone (DZ) SKUs (such as in Sweden Central or Germany West Central), which contractually guarantee that both data storage and processing remain within the specified geography.
Embedded Governance: Admins can enable a native integration within Microsoft AI Foundry at the subscription level. This automatically sends prompt and response data to Purview for auditing and compliance without requiring additional developer code.
Reference:
https://www.georgeollis.com/consuming-a-microsoft-foundry-agent-programmatically
NEW QUESTION # 29
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.
A company has a team that analyzes its customers by using a manual process.
You are designing an AI-based agent to automate and improve the process.
You need to recommend on which platform to build the agent. The solution must meet the following requirements:
- Use generative AI to answer common questions.
- Provide analytics to review AI performance.
- Identify customer demographics.
- Minimize custom development.
Solution: You recommend GitHub Copilot.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You recommend Microsoft Copilot Studio.
Copilot Studio is specifically designed to create customizable AI agents that automate business workflows with minimal coding.
Generative AI Answers: It uses "Generative Answers" to scan your specific data (websites, files, or SharePoint) to answer customer-related questions instantly.
Analytics Dashboard: It includes built-in analytics to track resolution rates, customer satisfaction (CSAT), and overall AI performance.
Customer Demographics: You can configure the agent to extract specific entities (like age, location, or industry) from conversations to build demographic profiles.' Low-Code/No-Code: It minimizes custom development by providing a visual interface to build logic, rather than writing raw code.
Incorrect:
* You recommend GitHub Copilot.
* You recommend Microsoft Security Copilot.
Reference:
https://www.phdata.io/blog/agentic-ais-and-introduction-to-copilot/
NEW QUESTION # 30
You need to design a Microsoft Copilot Studio agent for customer support. The agent must securely retrieve product warranty data from a REST API. The solution must minimize development effort. What should you include in the design?
Answer: A
Explanation:
The requirement is to build a Microsoft Copilot Studio agent that can securely retrieve product warranty data from a REST API while minimizing development effort.
The best choice is D. Create a custom connector in Copilot Studio and use the connector to call the API.
Why this is correct:
A custom connector is the standard low-code way to integrate Copilot Studio with a REST API. It lets the agent call the external warranty service securely, using supported authentication methods, without requiring heavy custom development. This directly satisfies both key requirements:
secure retrieval of warranty data
minimal development effort
NEW QUESTION # 31
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