Microsoft AB-100考古题推薦 & AB-100測試

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Microsoft AB-100 考試大綱：

主題簡介
主題 1
  • Deploy AI-powered business solutions: Focuses on deploying, testing, monitoring, and optimizing AI solutions in production. It also includes managing ALM processes, performance monitoring, and ensuring security, governance, and responsible AI compliance.
主題 2
  • Plan AI-powered business solutions: Focuses on analyzing business requirements and identifying where AI agents and generative AI can improve processes. It also includes defining AI strategy, evaluating ROI, and deciding whether to build, buy, or extend AI components.
主題 3
  • Design AI-powered business solutions: Covers designing AI agents, Copilot integrations, and intelligent workflows using platforms like Copilot Studio, Microsoft Foundry, and Dynamics 365. It includes planning prompts, connectors, agent behaviors, and solution extensibility.

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最新的 Microsoft Certified AB-100 免費考試真題 (Q37-Q42):

問題 #37
A company has a Microsoft Copilot Studio prompt-and-response agent.
You need to ensure that the agent meets the following requirements:
Provides effective and relevant responses
Provides conversational outcomes
Which metric should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:
* Provides effective and relevant responses # Generated answer rate and quality
* Provides conversational outcomes # Topics by outcome
Why "Generated answer rate and quality" is correct
The requirement says the agent must provide effective and relevant responses . In Microsoft Copilot Studio, the metric that most directly evaluates whether the agent is successfully generating useful answers is Generated answer rate and quality .
This metric helps assess whether the prompt-and-response agent is:
* returning answers consistently
* producing responses that are useful
* generating content of acceptable quality
* handling user requests with enough relevance
From an AI business solutions perspective, response effectiveness is not just about whether the agent says something. It is about whether the generated output is meaningful, accurate enough for the scenario, and valuable to the user. That is exactly what generated answer rate and quality is designed to measure.
This metric is especially important in prompt-and-response solutions because these agents depend heavily on the quality of generated outputs rather than only predefined topic flows.
Why "Topics by outcome" is correct
The second requirement says the agent must provide conversational outcomes . The best metric for understanding whether conversations are reaching meaningful end states is Topics by outcome .
This metric helps evaluate what happens to conversations, such as whether they:
* are resolved successfully
* escalate
* fail
* abandon
* complete a desired path
In enterprise AI and conversational business solutions, outcomes matter because stakeholders want to know whether the agent is actually driving the intended business result, not just generating text. A conversation can sound good but still fail operationally. Topics by outcome reveals whether the conversation reached a useful business conclusion.
For example, in a support or business-process scenario, leadership often wants to know:
* how many conversations were resolved
* how many required escalation
* which flows underperform
* where users get stuck
That is outcome measurement, and this metric aligns directly with that requirement.
Why the other metrics are not the best fit
Reactions
Reactions can provide feedback signals such as likes or dislikes, but they are not the strongest primary metric for determining whether responses are effective and relevant at a system level.
Satisfaction
Satisfaction is useful as a user sentiment metric, but it does not directly measure conversational outcomes. A user may be satisfied with tone but still not complete the intended business process.
Tool use
Tool use measures whether tools or actions are invoked, but it does not directly tell you whether responses are effective or whether conversations ended in successful outcomes.


問題 #38
A company plans to deploy a Microsoft Dynamics 365 Contact Center agent.
You need to ensure that the agent can transfer the conversation to a live customer service representative.
Which two components should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

答案：A,E

解題說明：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are B. Microsoft Copilot Studio and E. Customer engagement hub .
This question focuses on enabling a Dynamics 365 Contact Center agent to hand off a conversation to a live customer service representative . That requires both:
* the tool used to build and configure the conversational agent
* the service environment where live customer engagement and routing occur Why B. Microsoft Copilot Studio is correct Microsoft Copilot Studio is the platform used to build, configure, and manage the contact center agent experience. It enables you to define conversation flows, escalation logic, triggers, and handoff behavior.
In this case, the requirement is specifically that the agent must be able to transfer the conversation to a live representative. Copilot Studio is where that escalation or transfer behavior is designed as part of the agent experience.
Why E. Customer engagement hub is correct
The Customer engagement hub provides the operational environment for customer service interactions and live-agent engagement within Dynamics 365. Once the AI agent determines that escalation is required, the live representative needs an environment to receive and continue that engagement.
From a business solutions architecture perspective, this makes sense:
* Copilot Studio defines the agent and transfer logic
* Customer engagement hub supports the human service experience after transfer Together, they satisfy the end-to-end requirement for AI-to-human handoff.
Why the other options are incorrect
A). Microsoft Foundry
Foundry supports AI model and agent development scenarios, but it is not the specific component needed for live-agent transfer in Dynamics 365 Contact Center.
C). Microsoft 365 Agents Toolkit
This is not the core component for enabling Dynamics 365 Contact Center handoff to a live service representative.
D). an Azure AI Bot Service skill
Bot skills can extend capabilities, but they are not the primary required components for enabling the standard transfer from a Dynamics 365 Contact Center agent to a live customer service representative.
Expert reasoning:
For Contact Center escalation questions, think in two layers:
* agent authoring/orchestration # Microsoft Copilot Studio
* human service environment / live representative experience # Customer engagement hub


問題 #39
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.

答案：

解題說明：

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


問題 #40
A company has a Microsoft Copilot Studio agent that provides answers based on a knowledge base for customer support.
Users report that, occasionally, the agent provides inaccurate answers.
You need to use metrics from the Analytics tab in Copilot Studio to identify the cause of the inaccuracies.
Which two options should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

答案：B,C

解題說明：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are B. session information and session outcomes and E. quality of generated answers .
This scenario is focused on a knowledge base-driven Copilot Studio agent where users report that the agent sometimes gives inaccurate answers . The question asks which Analytics tab metrics should be used to identify the cause of those inaccuracies.
That means you need metrics that help you examine:
* how the answer was generated
* what happened in the conversation when the bad answer occurred
Why E. quality of generated answers is correct
This is the most direct metric for this scenario.
Because the agent is answering from a knowledge base , the problem is tied to the quality of the generated response itself. The quality of generated answers metric helps assess whether the generated responses are relevant, useful, and accurate enough for the user's request.
From an AI business solutions perspective, this metric is essential because it helps diagnose problems such as:
* weak grounding from the knowledge source
* irrelevant retrieval
* poor answer formulation
* hallucination-like behavior
* mismatch between user question and available source content
If the issue is inaccurate answers, the first place to investigate is the quality signal tied to generated answers.
Why B. session information and session outcomes is correct
To find the cause of inaccuracies, you also need to inspect the broader conversational context. Session information and session outcomes help you see:
* what the user asked
* how the agent responded
* whether the conversation was resolved
* whether the user abandoned, escalated, or retried
* where the conversation broke down
This is important because an inaccurate answer may not come only from poor generation quality. It may also come from:
* the way the user phrased the request
* lack of sufficient grounding context
* repeated failed attempts in a session
* escalation after an unhelpful answer
* patterns in unsuccessful conversations
In other words, quality of generated answers tells you about answer quality, while session information and outcomes help you understand the operational context in which those inaccuracies appear.
Together, these two give the strongest diagnostic view.
Why the other options are incorrect
A). survey results
Survey results can tell you whether users were happy or unhappy, but they do not directly help identify the cause of inaccurate knowledge-based responses. They are more of a feedback signal than a root-cause metric.
C). topic usage and topics with low resolution
This is more relevant for agents built around explicit topics and topic flows. The scenario specifically describes an agent that provides answers based on a knowledge base , so generated-answer analytics are more appropriate than topic-resolution analysis.
D). engagement, resolution, and escalation rates
These are useful high-level operational KPIs, but they are not the best metrics for diagnosing why answers are inaccurate. They show outcome trends, not the direct cause of answer-quality issues.


問題 #41
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.

答案：

解題說明：

Explanation:
Verified Answer : =
* 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.


問題 #42
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