P.S. GoShikenがGoogle Driveで共有している無料かつ新しいAB-100ダンプ：https://drive.google.com/open?id=1EgnXBumZX6CAPI8jjmhu_5Uxhx3trFyk
あなたは転職の状態にあるかもしれませんが、あなた自身のキャリアを持つことは信じられないほど難しいです。それからあなた自身を改善し、不可能な任務を可能にする方法はあなたの優先事項です。 AB-100試験に合格したい場合は、こちらからAB-100試験準備を行ってください。当社には、AB-100試験の合格を支援する、権威のある経験豊富なチームがいます。最も有用で有効なAB-100試験問題を取得できるだけでなく、AB-100試験に合格する方法に関する提案を取得することもできます。
| Section | Weight | Objectives |
|---|---|---|
| Design AI-powered business solutions | 25–30% | - Design extensibility and integration
|
| Deploy AI-powered business solutions | 40–45% | - Implement security, governance, compliance
|
| Plan AI-powered business solutions | 25–30% | - Evaluate costs and benefits
|
試験の結果は、GoShiken選択したAB-100学習教材と直接関係しています。 したがって、当社は試験のレビューに特に関心を持っています。 試験の証明書を取得することはほんの始まりです。 当社の練習資料は、広範囲に影響を与える可能性があります。 この種の試験に関する要求は、AB-100トレーニングクイズでMicrosoft満たすことができます。 ですから、私たちのAgentic AI Business Solutions Architect練習資料はあなたの未来にプラスの興味を持っています。 このような小さな投資でありながら大きな成功を収めたのに、Agentic AI Business Solutions Architectなぜあなたはまだためらっていますか？
質問 # 103
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.
正解：
解説：
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 that 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 knowledge source failed.
質問 # 104
What should you recommend to assist the CTO with the prebuilt agent selection process?
正解：C
解説：
The CTO wants to view available prebuilt agent templates in Dynamics 365 Supply Chain Management to decide which one should be prioritized for deployment. Agent management is the feature used to discover, review, and manage available agent templates and capabilities for Dynamics 365 business applications.
Why this is correct:
It supports discovering available prebuilt agents
It helps evaluate which agent can deliver the most business value
It aligns with the requirement to preview and assess candidate agents during the selection phase Why the other options are not correct:
B). Immersive Home is more of an experience surface, not the primary tool for selecting prebuilt agent templates C). Lifecycle Services (LCS) is used for environment and application lifecycle management, not for browsing Dynamics 365 AI agent templates D). Copilot Studio is primarily for building/customizing copilots and agents, not for selecting Dynamics 365 prebuilt Supply Chain Management agent templates
質問 # 105
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.
正解：
解説：
Explanation:
Restricts access to grounding data # Microsoft Purview access policies; Restricts model fine-tuning # Role- based access control (RBAC) in Microsoft Foundry Why Microsoft Purview access policies is correct The grounding data is stored in Microsoft Fabric, and the requirement is to restrict access to that data to only assigned roles.
That is a data governance and access control requirement. Microsoft Purview access policies are the best fit because they are designed to govern and control access to data across enterprise data estates. In this case, they help ensure that only authorized roles can access the grounding data used by the agents.
From an AI business solutions perspective, grounding data is often one of the most sensitive parts of the solution because it can contain:
proprietary business knowledge
internal documents
regulated operational information
contextual data used to shape model outputs
Purview helps enforce governed access to that data layer rather than relying only on general infrastructure controls.
Why RBAC in Microsoft Foundry is correct
The second requirement is to ensure that only the AI engineering team can perform model fine-tuning.
That is an action-level platform permission requirement. The best control for that is role-based access control (RBAC) in Microsoft Foundry.
RBAC allows the organization to assign permissions based on job function, so only authorized users or groups can:
create or modify fine-tuning jobs
manage model assets
update training configurations
control deployment-related AI resources
This is the right governance pattern because fine-tuning changes model behavior and can introduce:
security risk
compliance risk
quality drift
misuse of proprietary datasets
Restricting that capability to the AI engineering team through RBAC creates a clear separation of duties.
Why the other options are incorrect
Azure AI Content Safety
This is used to detect and filter harmful content. It does not control access to Fabric grounding data.
Azure Monitor alerts
Alerts help observe activity, but they do not enforce role-based access to data.
Azure Policy compliance rules
Azure Policy is useful for enforcing resource configuration standards, but it is not the best answer for role- based access to Fabric grounding data or for limiting fine-tuning actions to a specific team.
Azure Resource Manager (ARM) resource locks
Resource locks help prevent deletion or modification of Azure resources, but they do not provide the right permission model for controlling who can perform model fine-tuning operations.
Microsoft Entra Conditional Access
Conditional Access is mainly about sign-in and access conditions, such as device, location, or risk context. It is not the best direct control for restricting fine-tuning permissions inside Foundry.
Expert reasoning
Use this exam shortcut:
Need to control access to enterprise data # think Purview access policies Need to restrict who can perform AI platform actions like fine-tuning # think RBAC in the AI platform So the correct mapping is:
Restricts access to the grounding data: Microsoft Purview access policies Restricts model fine-tuning: Role-based access control (RBAC) in Microsoft Foundry
質問 # 106
Hotspot Question
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.
正解：
解説：
Explanation:
Box 1: Must be uploaded to the agent
Custom knowledge sources
Box 2: Must be enabled for the agent
AI general knowledge
To implement a generative AI agent for in-app help in Dynamics 365 Finance and Operations while minimizing inaccuracies, you must configure the agent in Microsoft Copilot Studio by uploading specific knowledge sources and enabling general AI knowledge.
1. Upload Custom Knowledge Sources
To ensure the agent provides accurate, organization-specific guidance, upload your internal documentation directly to the agent
2. Enable General AI Knowledge
To allow the agent to use its own broad generative AI knowledge for general inquiries:
Open Microsoft Copilot Studio and select the Dataverse environment linked to your Finance and Operations apps.
Navigate to Agents and open the specific agent named Copilot for finance and operations apps.
On the Overview tab, find the Knowledge section and set Allow the AI to use its own general knowledge to Enabled.
Publish the changes to make this capability available in the D365 F&O sidecar.
Reference:
https://arpideas.com/en/articles/knowledge-hub/building-smart-ai-agents-with-microsoft-copilot- studio
質問 # 107
A company has Microsoft Foundry agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
A company mandate states that the agents and their grounding data must adhere to data residency and movement regulations.
You need to recommend a governance solution for the agents.
What should you include in the recommendation?
正解：B
解説：
In this scenario, Microsoft Foundry agents and Azure OpenAI resources generate responses by using the Responses API. To ensure these agents adhere to data residency and movement regulations across the United States and Europe, Microsoft Purview should be included to provide the following governance and security controls:
Unified Data Discovery & Classification: Purview's discovery REST API allows orchestrator agents to identify relevant data assets (e.g., in Fabric or Databricks) across the organization's entire data landscape.
Sensitivity Label Enforcement: It ensures that AI-generated responses respect existing access controls by checking document label metadata at query time. This prevents oversharing of sensitive data and restricts users to authorized content.
Data Loss Prevention (DLP): By integrating Purview DLP policies, organizations can monitor, block, or warn when sensitive data is used in AI prompts or responses in real-time.
Data Residency Compliance: For strict European residency (e.g., GDPR), Azure OpenAI resources should be deployed using Data Zone (DZ) SKUs (such as in Sweden Central or Germany West Central), which contractually guarantee that both data storage and processing remain within the specified geography.
Embedded Governance: Admins can enable a native integration within Microsoft AI Foundry at the subscription level. This automatically sends prompt and response data to Purview for auditing and compliance without requiring additional developer code.
Reference:
https://www.georgeollis.com/consuming-a-microsoft-foundry-agent-programmatically
質問 # 108
......
私たちGoShikenに知られているように、AB-100認定は、急速な開発の世界の多くの現代人にとってますます重要になっています。 AB-100認定が多くの人にとってそれほど重要なのはなぜですか？認定を取得することは、人々がより良い仕事をしたり、より多くの富を得たり、より高い社会的地位を得るなど、夢を実現するのに役立つからです。多くの人々は、AB-100認定を正常に取得するのが困難です。また、試験の合格と認定の取得に問題がある場合は、AB-100クイズ準備を使用する時が来たと思います。
AB-100専門知識訓練: https://www.goshiken.com/Microsoft/AB-100-mondaishu.html
ちなみに、GoShiken AB-100の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1EgnXBumZX6CAPI8jjmhu_5Uxhx3trFyk