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AB-100試験情報 & AB-100問題数

AB-100試験情報 & AB-100問題数

by cajel54626@ · 229 things on Twos

一回だけでMicrosoftのAB-100試験に合格したい？Jpshikenは君の欲求を満たすために存在するのです。Jpshikenは君にとってベストな選択になります。ここには、私たちは君の需要に応じます。JpshikenのMicrosoftのAB-100問題集を購入したら、私たちは君のために、一年間無料で更新サービスを提供することができます。もし不合格になったら、私たちは全額返金することを保証します。
Microsoft AB-100 認定試験の出題範囲：
トピック出題範囲
トピック 1
AIを活用したビジネスソリューションの計画：ビジネス要件の分析と、AIエージェントや生成型AIがプロセスを改善できる箇所の特定に重点を置きます。また、AI戦略の策定、投資対効果（ROI）の評価、AIコンポーネントの構築、購入、拡張のいずれを選択するかの決定も含まれます。
トピック 2
AIを活用したビジネスソリューションの導入：本番環境におけるAIソリューションの導入、テスト、監視、最適化に重点を置きます。また、ALMプロセスの管理、パフォーマンス監視、セキュリティ、ガバナンス、責任あるAIコンプライアンスの確保も含まれます。
トピック 3
AIを活用したビジネスソリューションの設計：Copilot Studio、Microsoft Foundry、Dynamics 365などのプラットフォームを使用して、AIエージェント、Copilot統合、インテリジェントワークフローを設計する方法を解説します。プランニングプロンプト、コネクタ、エージェントの動作、ソリューションの拡張性についても説明します。
>> AB-100試験情報 <<
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Microsoft Agentic AI Business Solutions Architect 認定 AB-100 試験問題 (Q91-Q96):
質問 # 91
A company uses Microsoft Dynamics 365 Supply Chain Management.
You are designing an AI supply chain process that meets the following requirements:
Provides managers with AI-driven insights that surface key information from customer orders Helps planners use AI to anticipate future product needs more accurately You need to recommend which Microsoft Copilot features to include in the design.
What should you recommend 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:
Provide AI-driven insights from customer orders # AI Summaries with Copilot; Anticipate future product needs # Generative insights for Demand planning The first requirement is to give managers AI-driven insights that surface key information from customer orders .
That aligns best with AI Summaries with Copilot , because summaries are designed to extract and present the most important information from operational records in a concise, business-friendly way. In a supply chain context, this helps managers quickly understand:
important order details
exceptions or risks
priority items
fulfillment context
notable changes or issues tied to customer orders
From an AI business solutions perspective, this is exactly the kind of feature used to reduce manual review effort and improve decision speed. Rather than reading through many order records, managers get a synthesized view of key information.
Why "Generative insights for Demand planning" is correct
The second requirement is to help planners anticipate future product needs more accurately .
This directly maps to Generative insights for Demand planning . Demand planning is the business function focused on forecasting future demand, identifying trends, and improving planning accuracy for inventory and supply decisions.
Generative insights in this area help planners by surfacing patterns, explaining forecast behavior, and supporting better forward-looking decisions about product demand.
From an agentic AI business solutions standpoint, this is the right fit because it applies AI to:
forecast interpretation
trend identification
planning support
future demand anticipation
more accurate product need estimation
Why the other options are incorrect
Workload insights with Copilot
This is not the best match for surfacing key information from customer orders . It is more associated with operational workload visibility than customer-order summarization.
Microsoft Power BI
Power BI is useful for analytics and dashboards, but the question specifically asks for a Microsoft Copilot feature to anticipate future product needs. The direct feature match is Generative insights for Demand planning .
The Customer credit and collections workspace
This is focused on finance and collections activity, not on supply chain customer-order insight summarization.
Product information management
This manages product data and attributes, not AI-driven future demand anticipation.
The Supplier Communications Agent
This is related to supplier communication workflows, not demand forecasting for future product needs.
Expert reasoning
A quick exam shortcut here is:
Surface key information from records/orders # think AI Summaries with Copilot
Anticipate future demand/product needs # think Generative insights for Demand planning
質問 # 92
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 th at 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 kno wledge source failed.
質問 # 93
A company has a customer order system that creates sales orders manually.
You need to design an AI solution to automate the following tasks as part of the system:
Save the order details to a database.
Update the order status in the database.
Extract the order details from an order file.
Prepare and send a confirmation email to customers.
The solution must minimize development effort and support intelligent automation and solution integration.
What should you include in the design?
A. a workflow in Azure Logic Apps
B. a multi-agent solution that uses Microsoft Foundry Agent Service
C. a multi-agent solution that uses the Semantic Kernel SDK
D. a Microsoft Copilot Studio agent that uses Microsoft Power Automate workflows
正解：D
解説：
To create this system, you'll need to integrate Microsoft Copilot Studio with Power Automate and AI Builder. This combination allows your agent to not only "talk" but also "act" by executing complex backend workflows.
Solution Architecture
Front-end: A Copilot Studio agent serves as the interface, receiving order requests or files from customers.
Brain (Logic): Power Automate cloud flows act as the "skills" for your agent, handling all database and email operations.
Data Extraction: AI Builder (Document Processing) extracts specific fields (like Item ID, Quantity, or Customer Name) from uploaded order files.
Storage: Microsoft Dataverse or SQL Server functions as the database to save and update order records.
Reference:
https://medium.com/@sushmita.sg/build-an-ai-powered-customer-support-system-in-2-hours-no- code-using-microsoft-copilot-2e3275dbbee5
質問 # 94
Case Study 2 - Contoso, Ltd
Overview
Contoso, Ltd. is a high-tech manufacturing company that uses Microsoft Dynamics 365 Finance.
Dynamics 365 Supply Chain Management, and Dynamics 365 Commerce for its North American operations. The company designs and develops innovative products that have many patents and proprietary technologies. The patents and engineering designs are closely guarded secrets.
Contoso executives want to integrate and adopt AI solutions to help scale the company in preparation for an anticipated period of rapid growth.
The company has multiple legal entities and Azure subscriptions that will be used in the adopted AI solutions.
Requirements
AI Adoption
The following executives will have specific responsibilities in the overall AI adoption:
Chief Technology Officer (CTO): Select one Dynamics 365 Finance,
Dynamics 365 Supply Chain Management or Dynamics 365 Commerce prebuilt
AI agent and one custom Microsoft Copilot Studio AI agent to prioritize and deploy during the initial AI adoption phase.
Chief Information Officer (CIO): Ensure that appropriate security
labels are assigned to the data used by the AI agents.
Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the AI agents being deployed.
Chief Information Security Officer (CISO): Discover and inventory AI
resources for auditing.
Chief Executive Officer (CEO): Ensure that all solutions adhere to
industry-standard responsible AI practices.
All AI initiatives and agents will have a detailed business use case, a defined audience profile, and an estimated ROI that will compare the cost savings of the current process against the estimated costs of using the new AI solutions.
The company's research and development (R&D) department already has a custom Model Context Protocol (MCP) server that contains comprehensive product specifications and compliance data.
Prebuilt AI Agent
The CTO has NOT yet selected which prebuilt AI agent to use in Dynamics 365 Supply Chain Management. The CTO wants to view available agent templates to identify which agent will add the most business value.
Depending on which high-priority AI agents are identified, its agent capabilities must be previewed in a discovery meeting with the relevant business operation stakeholders.
Custom AI Agent
Contoso has identified the following custom AI agent requirements:
The custom AI agent will use data from Dynamics 365 Supply Chain
Management to answer questions for the manufacturing team as a low-code solution.
The custom AI agent will be accessible from within Microsoft Teams.
The custom AI agent must be designed to eventually connect to other
agents that can be selected based on their description.
The topics used in the custom AI agent will be selected based NOT on
a trigger phrase, but on a description of the purpose of the query, to
make the interactions more conversational.
The custom AI agent must be able to answer questions about product
specifications by using existing technologies. The product
specifications are maintained by the R&D department.
The custom AI agent must be integrated with and accessible from
Dynamics 365 Supply Chain Management.
The custom AI agent must be able to use Dynamics 365 Supply Chain
Management business logic that is stored outside of the application.
Analysis, Reporting, and Troubleshooting
Contoso has identified the following analysis, reporting, and troubleshooting requirements:
The CISO will audit all the AI solutions monthly for compliance and
security.
The CFO will analyze all the AI solutions quarterly to compare the
estimated ROI against actual measured efficiencies and adoption. The
CFO will use the Copilot Studio agent usage estimator to perform this
analysis.
The CISO wants to identify how much sensitive data was accessed for a given AI agent run and who accessed the data. Too much sensitive data accessed by a single user might indicate a high security risk.
The CTO wants to track user feedback on the quality of the AI agent
responses during user interactions with the agents. Consistently poor
feedback will trigger an escalated reengineering discussion.
The CEO wants a quarterly assessment of all the required metrics for
their specific responsibilities. The tools used for the assessments
must be Microsoft-recommended and must verify reliability,
interpretability, fairness, and compliance.
The CFO wants to identify how many interactions with the AI agents
are abandoned on a given day as compared to resolved conversations. Too many abandoned sessions might indicate that Copilot Studio credits are being used inefficiently by end users.
What should you recommend to assist the CEO with their specific responsibilities?
A. Microsoft Foundry Tools
B. the Microsoft Service Trust Portal
C. the Responsible AI dashboard
D. Microsoft Purview
E. Compliance Center
正解：C
解説：
Scenario: The CEO wants a quarterly assessment of all the required metrics for their specific responsibilities. The tools used for the assessments must be Microsoft-recommended and must verify reliability, interpretability, fairness, and compliance.
To ensure your Microsoft Dynamics 365 AI agent meets quarterly compliance and ethical standards, the Responsible AI (RAI) Dashboard in Azure Machine Learning is the primary recommended tool.
Microsoft recommends aligning quarterly assessments with their Responsible AI Standard v2, which focuses on six core principles: fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard
質問 # 95
A company deploys agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
You need to recommend a governance solution that meets the following requirements:
Enforces the deployment of the resources to only approved Azure regions Provides continuous compliance verification of the resources
正解：
解説：
Explanation:
Enforces deployment to only approved Azure regions # Azure Policy; Provides continuous compliance verification # Microsoft Defender for Cloud Why Azure Policy is correct The requirement is to enforce that Azure OpenAI resources can be deployed only in approved Azure regions.
That is exactly what Azure Policy is designed to do. Azure Policy allows organizations to create and assign rules that govern resource deployment and configuration. For regional restrictions, you can define a policy that permits deployments only in allowed locations and denies deployments elsewhere.
From an AI business solutions and cloud governance perspective, Azure Policy is the right preventive control because it acts at deployment time. It helps enforce organizational standards before noncompliant resources are created.
Typical policy use cases include:
restricting allowed Azure regions
enforcing approved SKUs
requiring tags
limiting resource types
ensuring security configuration standards
This is especially important for AI deployments where geography may affect:
regulatory compliance
data residency
internal governance
customer contract obligations
Why Microsoft Defender for Cloud is correct
The second requirement is to provide continuous compliance verification of the resources.
That points to Microsoft Defender for Cloud.
Defender for Cloud continuously assesses Azure resources against security and compliance standards. It provides visibility into resource posture, identifies misconfigurations, and tracks compliance status over time.
This makes it well suited for ongoing verification because it supports:
continuous assessment
compliance dashboards
security posture monitoring
recommendations for remediation
regulatory standard mapping
In enterprise AI deployments, this is critical because governance is not only about blocking bad deployments.
It is also about continuously validating that deployed resources remain compliant as environments evolve.