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>> AB-100 Valid Test Answers <<
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NEW QUESTION # 71
A company has a Microsoft Foundry generative Al model.
You need to evaluate the model ' s output to measure the overall quality and coherence of generated responses. The evaluation must use GPT-4o as a judge and return a numeric score for each output.
Which type of metric should you use?
Answer: D
Explanation:
The requirement says the evaluation must:
* measure overall quality and coherence of generated responses
* use GPT-4o as a judge
* return a numeric score for each output
That maps directly to AI quality (AI assisted) .
Why D is correct:
AI-assisted quality metrics use a strong model such as GPT-4o to evaluate generated outputs against qualitative dimensions like coherence, relevance, completeness, and overall response quality, and then assign scores.
Why the other options are not correct:
* A. Groundedness checks whether the answer is supported by the source/context, not overall quality and coherence.
* B. AI quality (NLP) usually refers to traditional NLP-style metrics rather than judge-model scoring with GPT-4o.
* C. risk and safety evaluates harmfulness, toxicity, jailbreak risk, or policy concerns, not overall response quality.
NEW QUESTION # 72
Drag and Drop Question
You are designing two Microsoft Copilot Studio agents named Agent1 and Agent2. Each agent must meet the following requirements:
- Each agent must use a standard model.
- Each agent must NOT use generative orchestration.
- Agent1 must support simple and short phrases for a given topic.
- Agent2 must integrate with Microsoft Dynamics 365 Contact Center
voice channel.
You need to recommend language models for the agents.
What should you recommend for each agent? To answer, drag the appropriate language models to the correct agents. Each language model 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:
Box 1: Natural Language Understanding (NLU)
Agent1 must support simple and short phrases for a given topic.
For a Microsoft Copilot Studio agent that must not use generative orchestration and requires support for simple, short trigger phrases, the best choice is the Classic NLU (Natural Language Understanding) model.
When you disable generative orchestration (also known as "Generative mode" or "Generative AI" orchestration), the agent reverts to Classic orchestration. In this mode, the agent relies on predefined trigger phrases to map user input directly to specific topics.
Box 2: Natural Language Understanding + (NLU +)
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.
For a Microsoft Copilot Studio agent using classic orchestration (no generative orchestration) and integrating with the Dynamics 365 Contact Center voice channel, the best language model is NLU+.
Why NLU+ is the Best Choice
While standard agents offer three "classic" Natural Language Understanding (NLU) options, NLU+ is specifically designed for high-performance, enterprise-grade scenarios like voice channels.
Note:
Comparison of Classic Models
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview
NEW QUESTION # 73
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 # 74
A company has a Microsoft Power Platform environment.
You need to build two agents named Agent1 and Agent2. The solution must meet the following requirements:
* Agent1 must be extendable by using the Semantic Kernel and must connect to multiple business apps and APIs.
* Agent2 must connect directly to data stored in Microsoft Dataverse and must be embeddable in a Microsoft Power Apps canvas a pp.
What should you use to build each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Agent1 # Microsoft Foundry
Agent2 # Copilot in Power Apps
Comprehensive and Detailed Explanation from Agentic AI Topics:
For Agent1, the requirement is that it must be extendable by using Semantic Kernel and connect to multiple business apps and APIs. The best fit is Microsoft Foundry because Foundry-based agents are designed for extensibility and developer-oriented orchestration, including integration patterns that work well with Semantic Kernel and external tools/APIs.
For Agent2, the requirement is that it must connect directly to Microsoft Dataverse and be embeddable in a Power Apps canvas app. The best fit is Copilot in Power Apps, because it is designed for Power Platform- native experiences, works naturally with Dataverse-backed app data, and is intended for embedding AI experiences inside canvas apps.
Why the other options are not the best match:
Azure Logic Apps is for workflow orchestration, not the primary platform for building these agents.
Microsoft Copilot Studio is strong for conversational agents, but the wording here points more directly to Power Apps-native embedding for Agent2 and Semantic Kernel extensibility for Agent1.
NEW QUESTION # 75
You use Microsoft Copilot Studio analytics to analyze the performance of a deployed Copilot Studio agent.
You need to identify which performance metrics to use to measure the following:
* The percentage of engaged sessions that are escalated to a live customer service representative
* The number of agent queries that cause a knowledge source error
What should you identify for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In Microsoft Copilot Studio analytics , each metric is designed to measure a different aspect of agent performance.
For the first requirement, the metric that tracks the percentage of engaged sessions escalated to a live customer service representative is Escalation rate . This directly measures how often conversations are handed off from the agent to a human.
For the second requirement, the metric th at helps identify queries causing a knowledge source error is Answer quality . This area evaluates how well the agent responds and includes issues related to grounded answers, failed responses, and knowledge-source-related problems.
Why the other options are not correct:
* Customer Satisfaction (CSAT) score measures user satisfaction, not escalation percentage or knowledge source errors.
* Engagement rate measures whether users actively interact with the agent, not whether sessions are escalated or whether a kno wledge source failed.
NEW QUESTION # 76
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