Microsoft Agentic AI Business Solutions Architect - AB-100 FREE EXAM DUMPS QUESTIONS & ANSWERS
Which two components for the custom Al agent should you include in the application lifecycle management (ALM) process? Each correct answer presents part of the solution.
NOTE; Each correct selection is worth one point.
NOTE; Each correct selection is worth one point.
Correct Answer: B,D
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You are designing a Microsoft Copilot Studio agent that uses a custom Microsoft Foundry model to generate responses. You need to ensure that the agent can securely connect to and invoke the custom model during user interactions. What should you include in the design?
Correct Answer: B
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A company has a Microsoft Copilot Studio agent for customer support. You are reviewing and validating the following prompts:
* A prompt that has instructions to " help the customer as best you can "
* A prompt that helps retrieve product information from a knowledge base You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
* A prompt that has instructions to " help the customer as best you can "
* A prompt that helps retrieve product information from a knowledge base You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
This question is about improving prompt quality so a Microsoft Copilot Studio agent gives consistent and accurate answers.
For the first prompt, "help the customer as best you can" is too vague. It does not tell the model exactly what task to perform, what boundaries to follow, or what kind of response is expected. The correct improvement is to rewrite the prompt with clear and task-specific instructions. Clear prompts reduce ambiguity and make agent behavior more predictable and repeatable.
For the second prompt, the agent is retrieving product information from a knowledge base. To keep answers accurate and grounded, the best practice is to use responses with only reference sources and limit the response scope. That ensures the model stays tied to approved knowledge and does not invent unsupported product details.
Why the other options are not correct:
Add filler words to make the prompt sound more natural and conversational does not improve accuracy or consistency.
Keep the prompt vague to enable model flexibility increases inconsistency.
Add several open-ended questions to give the model broader context can make responses less focused.
Remove the knowledge source so that the model responds freely with general product information would reduce reliability and increase hallucination risk.
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
Correct Answer: C
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What should you include in the custom Al agent design to meet the R & D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
To expose the data to the agent, create: a custom connector
Add to the agent: the MCP server
The custom agent must answer questions about product specifications and compliance information, and the case study states that the R & D department already has a custom Model Context Protocol (MCP) server containing that information.
The best design is:
create a custom connector to expose that external capability cleanly to the low-code Copilot solution add the MCP server to the agent so the agent can use that external knowledge/tooling source Why this is correct:
The MCP server already exists and contains the needed product-specification and compliance data.
In a Copilot/agent design, you need a way to expose external functionality and data in a reusable, secure way.
A custom connector is the low-code integration mechanism that fits this requirement.
Then the agent can use the MCP server as the connected external capability for answering those questions.
Why the other options are not correct:
Azure AI Bot Service channel is for communication channels, not for exposing this knowledge source.
a custom OData entity is not the right pattern for integrating the existing MCP-based capability.
the Semantic Kernel is a developer orchestration framework, but the requirement emphasizes using the existing MCP technology in a low-code solution.
an event trigger is unrelated to exposing R & D specification/compliance knowledge.
a REST API is too generic here; the scenario specifically points to the existing MCP server.
a tool is close conceptually, but the most direct answer choice tied to the existing R & D technology is the MCP server.
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?
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?
Correct Answer: A
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You are designing a testing solution for Microsoft Copilot Studio agents.
You need to validate prompt engineering best practices to ensure that the agents generate accurate and contextually relevant responses. Which prompt validation techniques and metrics should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to validate prompt engineering best practices to ensure that the agents generate accurate and contextually relevant responses. Which prompt validation techniques and metrics should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
To validate prompt engineering for Microsoft Copilot Studio agents, the goal is to confirm that the agent responds correctly even when users ask the same thing in different ways, and to measure whether the responses are actually useful and correct.
For the prompt validation technique, the correct choice is Use prompts that have varied phrasing. This is a core best practice because real users do not ask questions in one fixed form. They may use different wording, sentence structure, synonyms, or levels of detail. Testing with varied phrasing checks whether the prompt design is robust and whether the agent can still produce the right response across natural language variation.
For the metric, the correct choice is Response relevance and accuracy. Since the requirement is to ensure responses are accurate and contextually relevant, this is the most appropriate measure. It directly evaluates whether the output answers the user's need correctly and in the right context.
Why the other options are not correct:
Exclude domain-specific terminology from the prompts is not a best practice in business AI solutions. In many enterprise scenarios, domain-specific terms are essential for accuracy.
Use only simple, one-word prompts does not reflect real-world usage and would weaken testing coverage.
The number of words generated per response does not tell you whether the response is correct or contextually appropriate.
The response generation time is a performance metric, not the best metric for validating prompt quality.
You are designing an AI strategy for Microsoft Dynamics 365 finance and operations apps. You are evaluating the use of Microsoft Copilot Studio to provide in-app help and guidance based on generative AI general knowledge.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Custom knowledge sources # Must be uploaded to the agent; AI general knowledge # Must be disabled for the agent The requirement says the solution must minimize the risk of generating inaccurate responses. In generative AI business solutions, the best way to reduce hallucinations or loosely grounded answers is to rely on approved, scoped, domain-specific knowledge rather than broad general knowledge.
That means the agent should use:
custom knowledge sources that are intentionally provided to the agent
AI general knowledge disabled, so the agent does not answer from broad open-ended model knowledge when enterprise-specific guidance is needed In finance and operations scenarios, this is especially important because users may ask about:
internal business processes
financial controls
operational procedures
policy-specific guidance
company-configured ERP workflows
These are areas where inaccurate answers can create operational or compliance risk. Grounding the agent on uploaded custom sources provides a more controlled knowledge base.
Why custom knowledge sources must be uploaded
If the goal is in-app help and guidance for Dynamics 365 finance and operations apps, the safest and most reliable approach is to provide the agent with curated documentation such as:
internal process guides
approved SOPs
finance workflow documentation
policy documents
ERP-specific instructions
Uploading these custom knowledge sources ensures the agent answers from enterprise-approved material rather than from generic model knowledge.
From an AI governance perspective, this improves:
factual grounding
relevance
auditability
trustworthiness
alignment to company-specific processes
Why AI general knowledge must be disabled
General AI knowledge can be useful in broad assistant scenarios, but in a finance and operations help context it increases the risk that the agent may generate:
overly generic responses
answers not aligned to company policy
incorrect interpretations of internal process steps
content that sounds plausible but is not operationally correct
Because the question explicitly says to minimize the risk of inaccurate responses, the safer design is to disable general knowledge and constrain responses to approved custom sources.
A company processes invoices stored across multiple systems in multiple formats.
You need to implement an Al solution to automate the invoice processing. The solution must meet the following requirements:
* Automate multi-step invoice processing tasks, including document analysis, data validation, and approval routing.
* Enable users to interact directly via Microsoft Teams to review and approve invoices.
* Minimize development efforts to define and customize approval workflows.
What should you include in the solution?
You need to implement an Al solution to automate the invoice processing. The solution must meet the following requirements:
* Automate multi-step invoice processing tasks, including document analysis, data validation, and approval routing.
* Enable users to interact directly via Microsoft Teams to review and approve invoices.
* Minimize development efforts to define and customize approval workflows.
What should you include in the solution?
Correct Answer: D
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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.
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.
Correct Answer:
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.