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Fast2testは、Microsoft市場で入手できる他の試験教材とは異なり、AB-100学習トレントは、紙だけでなく携帯電話を使用して学習できるように、さまざまなバージョンを特別に提案しました。興味や習慣に応じて、AB-100トレーニングガイドのバージョンを選択できます。バリューパックを購入すると、3つのバージョンがすべて揃っており、価格は非常に優遇されており、すべての学習体験を楽しむことができます。これは、これら3つのバージョンがもたらすAgentic AI Business Solutions Architect利便性のために、いつでもどこでもAB-100試験エンジンを学習できることを意味します。
質問 # 68
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 uses Microsoft 365 and Dynamics 365.
You need to recommend a solution to automatically summarize email threads, generate suggested replies in Microsoft Outlook, and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend a classic Microsoft Dataverse workflow.
Does this meet the goal?
正解：B
解説：
Correct:
* You recommend Microsoft 365 Copilot for Sales.
Incorrect:
* You recommend a classic Microsoft Dataverse workflow.
* You recommend a Microsoft 365 Copilot agent template.
Note:
In the described scenario, Microsoft 365 Copilot for Sales acts as the primary bridge between your productivity tools and CRM data. It integrates directly into Microsoft Outlook and Teams to surface real-time insights from Dynamics 365 Sales or Salesforce.
Key capabilities for this specific workflow include:
Automated Email Summarization: Copilot scans long email threads in Outlook to extract key points, highlights, and BANT (Budget, Authority, Need, Timeline) data. If the sender is an external contact recognized in your CRM, the summary is automatically enriched with relevant account and opportunity data.
Suggested Email Replies: When replying to customer emails, Copilot generates drafts based on the context of the conversation and existing CRM data. You can use predefined response categories (e.g., "Reply to an inquiry," "Offer a proposal") or custom prompts to include specific opportunity details in the draft.
Meeting Preparation Summaries: Before a scheduled meeting, Copilot for Sales provides a
"preparation card" in Teams or Outlook. This summary includes:
- CRM Data: Matched opportunity and account attributes.
- Contextual History: Summaries of past email exchanges and the last three seller notes.
- Strategic Insights: Key risks, follow-up actions, and discussion points from previous interactions.
Reference:
https://msdynamicsworld.com/blog/microsoft-copilot-sales-close-deals-faster-ai
質問 # 69
A company uses Microsoft 365 and Dynamics 365
You need to recommend a solution to automatically summarize email threads, generate suggested replies in Microsoft Outlook, and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend a classic Microsoft Dataverse workflow.
Does this meet the goal?
正解：B
質問 # 70
A company has a cloud-based Al solution that uses Azure OpenAI models.
You need to design a monitoring solution that meets the following requirements:
* Monitors performance metrics and operational health for the models
* Monitors Al apps and agents for compliance
* Uses Azure-native capabilities
* Minimizes development effort
What should you use 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. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Verified Answer : =
* Monitors AI app and agents for compliance # Microsoft Purview
* Monitors performance metrics and operational health # Azure Monitor
For an Azure-based AI solution using Azure OpenAI models , the best Azure-native monitoring design is to separate:
* operational and performance monitoring
* compliance and governance monitoring
For performance metrics and operational health , the correct choice is Azure Monitor . It is the standard Azure-native service for collecting telemetry, tracking service health, monitoring metrics, analyzing logs, and alerting on runtime issues. This is the best fit for model and application operational monitoring with minimal development effort.
For monitoring AI apps and agents for compliance , the correct choice is Microsoft Purview . Purview is designed for compliance, governance, data protection, and policy-based oversight across data and AI-related assets. It aligns best with the requirement to monitor AI applications and agents from a compliance perspective.
Why the other options are not the best fit:
* Azure API Management is for API exposure, management, and security, not primary compliance or operational monitoring.
* Azure Policy is used to enforce and assess Azure resource compliance, but it is not the main tool for monitoring AI apps and agents in the broader compliance/governance sense asked here.
* Azure Stream Analytics is for streaming data processing, not this monitoring scenario.
* Microsoft Defender is focused on security threat detection and posture, not overall AI compliance monitoring.
質問 # 71
A company has a Microsoft 365 tenant in Canada and multiple Microsoft Power Platform environments in Canada and the United States. The company plans to deploy a Microsoft Copilot Studio agent to the Canadian environment that will use:
* Microsoft Dataverse data stored in Canada
* A connector that connects to an Azure OpenAI instance in the United States You need to ensure that the agent adheres to data residency and data movement policies before being deployed. What should you do?
正解：B
解説：
The key issue is that the agent will run in a Canadian environment and use:
Dataverse data stored in Canada
a connector to Azure OpenAI in the United States
That means data may need to move across regions. Before deployment, the organization must make sure this cross-region use is explicitly allowed under the platform's data movement and residency controls.
That makes C the correct answer.
Why C is correct:
It directly addresses the fact that the solution uses services in different geographic regions It ensures the environment and its connector dependencies are configured to allow that movement in line with platform policy It is the most specific action tied to data residency and data movement compliance before deployment Why the other options are not correct:
A). Ensure that the data processed by Azure OpenAI is stored in the United States.This does not address whether the cross-region movement itself is permitted.
B). From the Microsoft Purview portal, validate the Data loss prevention settings.DLP helps govern connector usage and data exfiltration, but the question is specifically about data residency and cross-region data movement.
D). Migrate the tenant to the United States.This is unnecessary and does not align with the stated Canadian deployment requirement.
質問 # 72
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 # 73
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Microsoft知ってほしいのは、人々が私たちの製造哲学の中心にいるということです。そのため、AB-100試験問題をより高度なものにする直感的な機能に重点を置いています。 したがって、AB-100ガイドトレントを使用すると、AB-100試験に最も効率的かつ生産的な方法で簡単に合格し、献身と熱意を持って勉強する方法を学ぶことができます。 Agentic AI Business Solutions Architect試験に合格して目標を達成するためのFast2test最良のツールでなければなりません。
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P.S. Fast2test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=18gog_vxZNSEKp4srf52tWph0Mf8396FW