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| Section | Weight | Objectives |
|---|---|---|
| Deploy and Monitor Agentic AI Solutions | 20-25% | - Implement error handling and logging - Configure monitoring and analytics - Publish and deploy agent solutions - Optimize agent performance |
| Design Agentic AI Solutions | 30-35% | - Define agent capabilities and boundaries - Design multi-agent architectures - Identify business scenarios for agentic AI - Select appropriate AI models and services |
| Implement Agentic AI Solutions | 30-35% | - Implement data connections and plugins - Set up agent-to-agent communication - Configure security and compliance settings - Configure AI agents using Microsoft Copilot Studio |
| Governance and Best Practices | 10-15% | - Apply responsible AI principles - Implement data privacy and security controls - Ensure regulatory compliance |
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質問 # 90
A startup wants to build a customizable, agent-based workflow that can integrate with their internal APIs, retrieve contextual data from various sources, and run complex business logic autonomously. The team has moderate engineering skills but explicitly wants to avoid the overhead of managing underlying infrastructure. Which Microsoft AI service model best fits this requirement for building and deploying their agent-based solution?
正解：B
解説：
PaaS (Microsoft Foundry + Azure OpenAI + Azure AI Search + Prompt Flow) is correct because Platform-as-a-Service models, particularly those leveraging Azure AI services, provide the necessary tools and flexibility for building custom AI agents and orchestration. Microsoft Foundry (formerly Azure AI Foundry) offers a comprehensive environment, Azure OpenAI provides the LLM power, Azure AI Search enables contextual data retrieval, and Prompt Flow helps with orchestration and evaluation. Crucially, PaaS abstracts away infrastructure management, aligning with the startup's requirement.
References:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/prompt-flow
https://www.microsoft.com/en-us/ai
質問 # 91
A company has Microsoft Copilot Studio agents.
The company plans to deploy custom connectors across development, test, and production environments.
You need to design an application lifecycle management (ALM) process to ensure consistency and prevent direct editing in production. Which two actions should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、C
質問 # 92
A company has a Microsoft Power Platform solution that contains the following components:
* Microsoft Dataverse tables
* A Microsoft Power Bl workspace named WS1
* A canvas app named App1 that uses Dataverse
* A Power Bl semantic model that connects to Dataverse by using DirectQuery You plan to use generative Al to provide answers to queries based on a subset of corporate data. You need to ensure that the data is available as a grounding data source for Al systems. What should you do?
正解：D
解説：
The goal is to use generative AI to answer questions based on a subset of corporate data, and to ensure that this data is available as a grounding data source.
The solution includes:
Dataverse tables
a Power BI workspace
a canvas app
a Power BI semantic model using DirectQuery to Dataverse
The best action is D. Endorse the semantic model.
Why D is correct:
Endorsing a semantic model makes it a trusted, discoverable enterprise data asset It is the appropriate step when you want approved corporate data to be used reliably by downstream AI and analytics experiences It fits the requirement of exposing a curated subset of data as a grounding source rather than duplicating or manually exporting it Why the other options are not correct:
A). Export the semantic modelExporting does not make it a governed grounding source.
B). Share WS1Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
C). Populate a Dataverse tableThe data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
質問 # 93
You need to design a Microsoft Copilot Studio agent that meets the following requirements:
Supports interactive speech responses
Optimizes decision-making and the accuracy of responses
What should you include in the design for each requirement? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all.
正解：
解説：
Explanation:
Supports interactive speech responses # Copilot Studio voice features; Optimizes decision-making and response accuracy # A deep reasoning model Why Copilot Studio voice features is correct The requirement is to design a Microsoft Copilot Studio agent that supports interactive speech responses.
Since the scenario is specifically centered on a Copilot Studio agent, the most direct and appropriate design choice is Copilot Studio voice features.
These voice features are intended to enable conversational voice experiences within the Copilot Studio environment, including spoken interaction patterns for agent-based experiences. In a business solutions context, this is the feature set that aligns most directly with building a voice-capable agent rather than just adding a lower-level speech technology component.
Why not the others for this requirement:
Azure AI Speech is a foundational speech service, but the question is about what to include in the design of a Copilot Studio agent. The more direct answer is the native Copilot Studio voice features.
SSML helps control how speech is synthesized, such as pronunciation, pacing, and emphasis, but it does not itself provide the full interactive speech response capability.
Azure Language in Foundry Tools is not the right fit for voice response functionality.
Why a deep reasoning model is correct
The second requirement is to optimize decision-making and the accuracy of responses. That points to a model capability that improves reasoning quality, response evaluation, and more structured inference. The best fit among the choices is a deep reasoning model.
A deep reasoning model is designed to better handle:
multi-step logic
more complex decisions
higher-quality answer generation
improved contextual inference
stronger response accuracy in nuanced scenarios
From an agentic AI business solutions perspective, this matters when the agent is expected not just to respond conversationally, but to produce answers that are more reliable and better aligned to business intent. For enterprise agents, reasoning quality often has a direct effect on trust, adoption, and operational outcomes.
Why the other options are incorrect
Azure AI Speech for decision-making and response accuracy
Azure AI Speech handles speech-related capabilities, not reasoning quality.
Azure Language in Foundry Tools for decision-making optimization
Language tooling can help in language-related scenarios, but it is not the best answer here for improving reasoning and decision quality compared to a deep reasoning model.
SSML for interactive speech responses
SSML enhances synthesized speech output, but it does not serve as the primary capability for interactive speech-based agent conversations.
Expert reasoning
For exam-style mapping:
Voice interaction in Copilot Studio # Copilot Studio voice features
Higher-quality reasoning, decisions, and response accuracy # a deep reasoning model
質問 # 94
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
A company has a Microsoft Dynamics 365 Sales environment that has Microsoft Copilot enabled.
You need to customize Copilot by tailoring how opportunity summaries are generated or how they are presented to users.
Solution: You configure AI Builder lead scoring models to influence opportunity summaries.
Does this meet the goal?
正解：B
解説：
Correct:
* You add fields to the opportunity summary.
Incorrect:
* You build Microsoft Power Automate flows to trigger customized Copilot summaries.
* You configure AI Builder lead scoring models to influence opportunity summaries.
Note:
To customize Microsoft Copilot opportunity summaries and incorporate AI Builder lead scoring data, you must configure the specific fields Copilot uses to ground its generative AI outputs.
By default, Copilot for Sales generates summaries using a set of predefined fields. To "influence" these summaries with scoring data, you need to add the predictive score and grade fields to the Opportunity summary configuration.
Step-by-Step Configuration
Switch Area: In the Sales Hub app, select the Change area menu in the bottom-left corner and choose App Settings.
Navigate to Copilot: Under General Settings, select Copilot.
Select Entity: Choose the Opportunities tab.
Add Fields:
Click Add fields.
Select the checkboxes for the fields you want to include.
You can select out-of-the-box, custom, and related table fields.
Save: Click Add and then Save your changes to update the summary configuration.
Reference:
https://learn.microsoft.com/en-us/dynamics365/sales/copilot-configure-summary-fields
質問 # 95
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AB-100試験に合格すると多くのメリットが得られることは誰もが知っていますが、Microsoftすべての受験者がそれを達成するのは容易ではありません。 AB-100ガイド急流は、すべての受験者が試験に合格するのを支援することを目的としたツールです。 私たちの試験資料は、コンピュータと人の量に制限なしでインストールおよびダウンロードできます。 弊社が提供するAB-100学習資料が有用であり、テストに合格するのに役立つことを保証します。 製品を購入すると、便利な方法を使用して、いつでもどこでもAB-100試験トレントを学習できます。 そのため、購入の前後に安心して、AB-100学習教材にウイルスがないことを信頼してください。 Agentic AI Business Solutions Architect当社の製品JPNTestに慣れるために、AB-100学習教材の機能と利点を次のようにリストします。
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