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NEW QUESTION # 38
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: A
Explanation:
https://learn.microsoft.com/en-us/azure/governance/policy/overview
NEW QUESTION # 39
A company uses Microsoft 365 and Dynamics 365
You need to recommend a solution lo automatically summarize email threads, generate suggested replies in Microsoft Outlook and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend a classic Microsoft Dataverse workflow.
Does this meet the goal?
Answer: A
NEW QUESTION # 40
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 # 41
Hotspot Question
You need to recommend a Microsoft Power Platform solution for customer support. The solution must include AI capabilities in Microsoft Power Automate and must meet the following requirements:
Use a centralized workspace for AI models.
Generate short overviews from large amounts of unstructured text, such as case notes or transcripts, without requiring additional training or coding.
What should you include in the recommendation for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 42
A company has a customer order system that creates sales orders manually.
You need to design an AI solution to automate the following tasks as part of the system:
- Save the order details to a database.
- Update the order status in the database.
- Extract the order details from an order file.
- Prepare and send a confirmation email to customers.
The solution must minimize development effort and support intelligent automation and solution integration.
What should you include in the design?
Answer: B
Explanation:
To create this system, you'll need to integrate Microsoft Copilot Studio with Power Automate and AI Builder. This combination allows your agent to not only "talk" but also "act" by executing complex backend workflows.
Solution Architecture
Front-end: A Copilot Studio agent serves as the interface, receiving order requests or files from customers.
Brain (Logic): Power Automate cloud flows act as the "skills" for your agent, handling all database and email operations.
Data Extraction: AI Builder (Document Processing) extracts specific fields (like Item ID, Quantity, or Customer Name) from uploaded order files.
Storage: Microsoft Dataverse or SQL Server functions as the database to save and update order records.
Reference:
https://medium.com/@sushmita.sg/build-an-ai-powered-customer-support-system-in-2-hours-no- code-using-microsoft-copilot-2e3275dbbee5
NEW QUESTION # 43
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