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NEW QUESTION # 64
A company has an AI solution that uses Azure OpenAI models.
You need to recommend a governance solution that monitors and audits changes to model configurations and data usage. The solution must minimize administrative effort.
What should you include in the recommendation?
Answer: A
Explanation:
Azure Monitor is the primary service for monitoring and auditing Azure OpenAI model configurations and data usage. By combining Azure Monitor with diagnostic settings, you can track management operations, analyze token consumption, and audit prompt/response data.
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/fundamentals/overview
NEW QUESTION # 65
Hotspot Question
A company uses Microsoft Dynamics 365 Finance for accounts payable and customer debt recovery.
You are designing an AI finance process that meets the following requirements:
- Provides AI-driven details to help staff identify overdue vendor
invoices and outstanding balances
- Helps staff reduce how long it takes to review overdue invoices and
payment history
You need to recommend which Microsoft Copilot features to include in the design.
What should you recommend 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: AI Summaries with Copilot
In Dynamics 365 Finance and Supply Chain Management, AI Summaries with Copilot are available on "All vendors" and "Purchase orders" detail pages. These summaries specifically highlight critical account information such as the number of overdue vendor invoices, outstanding balances, and payment status directly within the workspace.
Box 2: Collections coordinator summary
The Collections coordinator summary is specifically designed for collections agents who manage customer debt. It uses AI to consolidate vast amounts of data - including aged balances, payment history for the past year, and invoices for the last six months - into a single, concise overview.
This eliminates the need for staff to navigate multiple pages and manual lookups, significantly reducing the "average handling time" for reviewing account status.
Reference:
https://learn.microsoft.com/en-us/dynamics365/supply-chain/get-started/copilot-summaries-overview
https://learn.microsoft.com/en-us/dynamics365/finance/accounts-receivable/collectionscoordinatorsummary
NEW QUESTION # 66
A company has an Al solution named Solution1 that is deployed to the production environment. Solution!
uses an Azure OpenAI model to generate marketing emails for existing customers.
During an internal review, you identify that Solution1 creates different emails depending on the customers ' traits.
You need to recommend a strategy to mitigate the bias. The strategy must adhere to Microsoft responsible Al principles.
What should you recommend?
Answer: B
Explanation:
The scenario describes a deployed AI solution using Azure OpenAI that exhibits bias (creating disparate outcomes based on customer traits). This directly impacts the Fairness principle of Microsoft ' s Responsible AI framework.
Why " Modify the system instructions " is the Correct Strategy:
Direct Control via System Metaprompts: In large language model (LLM) applications like those powered by Azure OpenAI, the system instructions (or system message) define the behavior, constraints, and tone of the model. By modifying these instructions, you can explicitly direct the model to treat all customer segments equitably and ignore specific sensitive traits when drafting marketing content.
Mitigation without Re-engineering: * Option B and D (Training/Retraining): Azure OpenAI models are foundation models. Most companies use them via API and do not have access to the original " training dataset
" to modify it. While fine-tuning is possible, it is significantly more expensive and complex than prompt engineering.
Option C (Randomization): Randomization does not solve bias; it creates inconsistency and potentially irrelevant content, violating the Reliability and Safety principle.
Alignment with Responsible AI: Microsoft ' s documentation on Fairness recommends " Instructional Mitigation. " This involves adding specific rules to the system prompt, such as: " You must ensure the tone and value proposition of the email remain consistent across all demographic groups " or " Do not use customer traits such as age or gender to influence the core marketing message. "
NEW QUESTION # 67
Scenario: A development team is building a complex, multi-step agent using the Microsoft Agent Framework. This agent needs to receive a high-level user request, break it down into the required sequence of internal API calls and knowledge base, and manage the execution flow of these steps to achieve the final outcome.
Within the Agentic Core, which specific sub-component is primarily responsible for analyzing the user's intent, determining the optimal sequence of required tools/APIs and data sources (RAG), and managing the logical execution flow of these steps?
Answer: B
Explanation:
The Planner and Orchestrator is correct because this component is the "brain" of the Agentic Core. Its function is to take the user prompt, use the LLM to assist in decomposition, build a logical plan (Planner) of steps to solve the request, and then manage the execution of those steps using the available Tools and data sources (Orchestrator).
References:
https://learn.microsoft.com/en-us/agent-framework/media/agent.svg
https://techcommunity.microsoft.com/blog/educatordeveloperblog/ai-agents-planning-and- orchestration-with-the-planning-design-pattern---part-7/4399204
https://www.microsoft.com/en-us/microsoft-365/planner/microsoft-planner
NEW QUESTION # 68
Hotspot Question
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:
Box 1: Microsoft Purview Access Policies
Restricts access to the grounding data to only assigned roles
To secure and manage grounding data from Microsoft Fabric for Azure OpenAI agents and fine- tuned models, you can use Microsoft Purview to enforce role-based access and data protection policies.
Note:
Securing Data with Microsoft Purview & RBAC
Access Control Policies: Microsoft Purview enables role-based access controls (RBAC) over Fabric items, ensuring that when an AI agent retrieves data, it only accesses information the user is permitted to see.
Sensitivity Labels: Data in Fabric can be labeled (e.g., "Confidential"). Purview policies can restrict AI agents from accessing or acting upon content that violates these security labels.
OneLake Security: Fine-grained security in Fabric (Row-Level Security and Column-Level Security) is automatically honored by agents, guaranteeing that even with access to a dataset, sensitive PII (Personally Identifiable Information) can be restricted.
Box 2: Role-based access control (RBAC) in Microsoft Foundry
Restricts model fine-tuning to only the AI engineering team
Azure role-based access control (Azure RBAC) is used to manage and restrict access to AI resources, including the ability to perform fine-tuning operations. Platform administrators can assign specific roles and permissions (e.g., to AI engineers or data scientists) and use Azure Policy to implement fine-grained control over who can initiate fine-tuning jobs or deploy custom models within the Azure AI Foundry environment. This ensures the governance of the fine-tuning process.
Reference:
https://dynamicscommunities.com/ug/fabric-ug/preview-of-onelake-security-unified-data-access- control-for-data-enterprise
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control
NEW QUESTION # 69
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