P.S. Xhs1991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=1DZefSpqFQN5tQySKHDih3nDJJAK_Oc3v
Xhs1991のMicrosoftのAB-100試験トレーニング資料が受験生の皆様の評判を取ったのはもう最近のことではないです。これはXhs1991のMicrosoftのAB-100試験トレーニング資料は確かに信頼できて、受験生の皆様が首尾よく試験に合格することに助けを差し上げられることが証明されました。 Xhs1991のMicrosoftのAB-100試験トレーニング資料がベストセラーになって、他のサイトをずっと先んじて皆様の認可を取りましたから、好評は言うまでもないです。 MicrosoftのAB-100認定試験を受けたら、速くXhs1991というサイトをクリックしてください。あなたがずっとほしいものを手に入れることができますから。最もプロな人々が注目しているIT専門家になりたかったら、後悔しないように速くショッピングカートを入れましょう。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design Agentic AI Solutions | 30-35% | - Define agent capabilities and boundaries - Identify business scenarios for agentic AI - Design multi-agent architectures - Select appropriate AI models and services |
| Topic 2: Deploy and Monitor Agentic AI Solutions | 20-25% | - Optimize agent performance - Implement error handling and logging - Configure monitoring and analytics - Publish and deploy agent solutions |
| Topic 3: Governance and Best Practices | 10-15% | - Implement data privacy and security controls - Apply responsible AI principles - Ensure regulatory compliance |
| Topic 4: Implement Agentic AI Solutions | 30-35% | - Configure AI agents using Microsoft Copilot Studio - Set up agent-to-agent communication - Implement data connections and plugins - Configure security and compliance settings |
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質問 # 68
A key stakeholder in your organization proposes immediately deploying Microsoft Copilot across all business units. They believe that AI tools will automatically generate superior, data-driven decisions from day one, regardless of the current quality of organizational data or the alignment of existing business processes.
Based on Microsoft's AI for Business guidance, should you agree that deploying AI tools like Copilot will automatically lead to better decisions even if the organization does not first establish high-quality business data and well-defined, aligned workflows? [Select Yes or No]
正解：A
解説：
According to Microsoft's AI for Business guidance, AI tools like Microsoft Copilot do not automatically produce better decisions on day one if the organization lacks:
- High-quality, well-governed data
- Clearly defined and aligned business processes
- Proper data access, security, and governance
AI systems rely heavily on the quality, structure, and accessibility of underlying data. If the data is inconsistent, incomplete, or poorly managed, AI outputs can also be inaccurate or misleading.
Microsoft emphasizes establishing a strong data foundation and process alignment first before scaling AI across business units.
So it is not correct to assume AI alone will automatically generate superior decisions regardless of data quality or workflow readiness.
References:
https://www.microsoft.com/en-in/microsoft-copilot/copilot-101/ai-for-business#Customerexperience
https://www.microsoft.com/en-in/microsoft-365/business-insights-ideas/resources/grow-your-small-business-with-artificial-intelligence
質問 # 69
Case Study 1 - Fabrikam, Inc
Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam's infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source - a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company's objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:
- Use low-code development to create a single AI agent that has
Dataverse as its core component.
- Ensure that sales managers can access unanswered correspondence from
prospects and intervene as appropriate.
- Replace the previous proprietary software with Dynamics 365 Sales to
track sales cycles and customer interactions.
- Have the sales executives use Dynamics 365 Sales to track
interactions for open opportunities and send follow-up communications
to prospects.
- Have the sales executives use handsfree headsets to interact with an
AI agent when they have questions about internal policies or customer
data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
- Azure must be used for all future infrastructure workloads.
- The company must follow Microsoft-recommended methodologies for
infrastructure migration to the cloud.
- Any created AI agents must have their return on investment (ROI)
calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
- The final AI agent must follow Microsoft recommendations for a
conversational user experience.
- A designated checklist must be reviewed to ensure that the AI agent
follows Microsoft deployment recommendations for a compliant solution.
- Detailed telemetry must be logged for the first created AI agent to
help troubleshoot and optimize the agent during the initial AI agent
adoption process.
- Unexpected AI agent actions must end in an escalation to a live
representative. For example, a sales executive must be rerouted to a
representative if the agent cannot answer a question after two failed
attempts.
- The return on investment (ROI) of switching from the current process
to the future process is required for stakeholder sign off.
- The sales team must use Dynamics 365 Sales to correspond with
prospects more quickly and efficiently than currently.
- Sales managers must report on the adoption of the AI agent to key
Fabrikam stakeholders on a monthly basis.
- Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
Hotspot Question
Which framework should you use to meet the AI agent requirements for the sales cycle enablement? 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: the ALM Accelerator for Microsoft Power Platform
For Microsoft Copilot Studio best practices
Using the ALM Accelerator for Microsoft Power Platform is a recommended approach for managing the lifecycle of a low-code AI agent (Copilot Studio) that relies on Dataverse. It enables source control, versioning, and automated deployment of AI agents to ensure they follow Microsoft's best practices.
Box 2: Microsoft Power Platform Well-Architected framework
For conversational user experience
Utilizing the Microsoft Power Platform Well-Architected framework for a low-code AI agent (built in Copilot Studio) with Dataverse as the core data component ensures the solution is secure, reliable, and provides a high-quality conversational user experience (CUX). The framework helps align the agent with Microsoft's best practices for responsible AI, efficiency, and user satisfaction.
Scenario:
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
*-> The final AI agent must follow Microsoft recommendations for a conversational user experience.
Sales Cycle Enablement
To achieve the company's objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle
*-> Use low-code development to create a single AI agent that has Dataverse as its core component.
Reference:
https://learn.microsoft.com/en-us/power-platform/guidance/alm-accelerator/overview
https://learn.microsoft.com/en-us/training/modules/adopt-ai-agent-best-practice
質問 # 70
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 # 71
A financial services company uses Microsoft Dynamics 365 Finance.
Currently, the company ' s support staff manually reviews customer transaction histories to detect potential fraud cases before escalating the cases.
You need to recommend an automation solution for the review process. The solution must ensure that escalations reach a human analyst for final decision making. What should you recommend?
正解：C
解説：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is C. Configure a task agent to generate fraud risk scores for the human analyst to review .
This scenario is a classic human-in-the-loop AI business solution use case. The company wants to automate part of the fraud review process, but it also requires that final escalation decisions remain with a human analyst . That means the right solution is not full autonomy. It is decision support .
A task agent that generates fraud risk scores is the best fit because it allows AI to:
* analyze transaction history faster than manual review
* identify suspicious patterns
* prioritize cases
* reduce analyst workload
* preserve human oversight for final judgment
This design aligns with responsible AI and regulated-industry practices. In financial services, fraud detection often involves compliance, risk, and audit requirements. Because of that, the best architecture is usually one where AI assists with triage and recommendation , while a human makes the final decision.
Why the other options are incorrect:
A). Deploy an autonomous agent that closes non-fraud cases automatically This removes too much human oversight. The question explicitly requires that escalations reach a human analyst for final decision making . In fraud workflows, automatically closing cases can create regulatory, legal, and operational risk.
B). Use Microsoft 365 Copilot in Word to automatically finalize fraud detection policies This does not address the operational review process. It is about document productivity, not transaction review automation.
D). Export the data to a data lake for analysis in Microsoft Power BI
This may help reporting and analytics, but it does not directly automate the review-and-escalation workflow.
Power BI is primarily for visualization and analysis, not real-time task-level fraud triage.
Expert reasoning:
When the requirement says:
* automate the review process
* keep a human in final control
* support case escalation
the best answer is usually an assistive agent that scores or classifies risk for human review, not a fully autonomous one.
質問 # 72
A company has an AI solution that uses Azure OpenAI models.
You need to recommend a governance solution that monitors and audits changes to model configurations and data usage. The solution must minimize administrative effort.
What should you include in the recommendation?
正解：D
解説：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is E. Microsoft Purview.
This question is centered on governance, specifically the need to:
monitor changes to model configurations
audit data usage
minimize administrative effort
That combination points most strongly to Microsoft Purview.
Why E is correct
Microsoft Purview is Microsoft's core platform for data governance, compliance, auditing, information protection, and lifecycle oversight. When an organization is using Azure OpenAI models and needs a governance-oriented solution for monitoring and auditing how data is used, Purview is the best fit among the listed options.
From an AI business solutions perspective, governance is broader than infrastructure monitoring. It includes:
understanding how sensitive data is handled
tracking access and usage patterns
supporting audit and compliance needs
helping investigate data exposure concerns
enforcing information governance practices across AI-enabled workloads
Purview is especially strong when the requirement includes auditing data usage because that is a governance and compliance concern, not just a performance or telemetry concern.
It also minimizes administrative effort because it provides centralized governance capabilities rather than requiring the company to stitch together multiple lower-level services for oversight.
Why the other options are incorrect
A). Azure Monitor
Azure Monitor is useful for telemetry, logs, metrics, and operational monitoring. It helps observe system performance and activity, but it is not the best primary governance solution for auditing data usage and broader compliance oversight.
B). Azure Stream Analytics
This service is used for real-time stream processing and analytics. It does not address governance and audit requirements for Azure OpenAI model configurations and data usage.
C). Azure API Management
API Management helps publish, secure, and manage APIs. It is valuable for access mediation and control, but it is not the main governance and auditing platform for data usage and model-configuration oversight.
D). Azure Policy
Azure Policy is very strong for enforcing resource configuration standards and compliance rules at deployment and configuration time. However, the question also emphasizes auditing data usage, which is better aligned to Purview's governance capabilities. Policy is more about enforcement of resource state; Purview is stronger for governance, auditing, and data oversight.
Expert reasoning
Use this exam shortcut:
Need operational logs and metrics # Azure Monitor
Need deployment/configuration enforcement # Azure Policy
Need data governance, auditing, compliance, and information oversight # Microsoft Purview Because the question emphasizes both changes and data usage auditing with a governance lens, Microsoft Purview is the strongest answer.
So the correct choice is:
質問 # 73
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