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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Related Certifications: | Microsoft Applied Skills |
| Exam Format: | Multiple choice, Scenario-based |
| Certificate Validity Period: | 1 year (for recertification) |
| Exam Price: | USD $85 |
| Real Exam Qty: | Around 40-50 questions |
| Exam Duration: | 50 minutes |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | English |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic understanding of Microsoft Copilot, Azure AI services, and general AI/ML concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/applied-skills/agentic-ai-business-solutions-architect/ |
JPNTestのAB-100問題集というものをきっと聞いたことがあるでしょう。でも、利用したことがありますか。「JPNTestのAB-100問題集は本当に良い教材です。おかげで試験に合格しました。」という声がよく聞こえています。JPNTestは問題集を利用したことがある多くの人々からいろいろな好評を得ました。それはJPNTestはたしかに受験生の皆さんを大量な時間を節約させ、順調に試験に合格させることができますから。
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質問 # 68
A company has a Microsoft Power Platform environment.
You need to build two agents named Agent1 and Agent2. The solution must meet the following requirements:
* Agent1 must be extendable by using the Semantic Kernel and must connect to multiple business apps and APIs.
* Agent2 must connect directly to data stored in Microsoft Dataverse and must be embeddable in a Microsoft Power Apps canvas a pp.
What should you use to build each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Agent1 # Microsoft Foundry
Agent2 # Copilot in Power Apps
Comprehensive and Detailed Explanation from Agentic AI Topics:
For Agent1, the requirement is that it must be extendable by using Semantic Kernel and connect to multiple business apps and APIs. The best fit is Microsoft Foundry because Foundry-based agents are designed for extensibility and developer-oriented orchestration, including integration patterns that work well with Semantic Kernel and external tools/APIs.
For Agent2, the requirement is that it must connect directly to Microsoft Dataverse and be embeddable in a Power Apps canvas app. The best fit is Copilot in Power Apps, because it is designed for Power Platform- native experiences, works naturally with Dataverse-backed app data, and is intended for embedding AI experiences inside canvas apps.
Why the other options are not the best match:
Azure Logic Apps is for workflow orchestration, not the primary platform for building these agents.
Microsoft Copilot Studio is strong for conversational agents, but the wording here points more directly to Power Apps-native embedding for Agent2 and Semantic Kernel extensibility for Agent1.
質問 # 69
A company plans to deploy a Microsoft Foundry agent.
You need to recommend an application lifecycle management (ALM) process to ensure that the agent evaluates against baseline accuracy metrics before being deployed.
What should you recommend?
正解：B
解説：
To implement an Application Lifecycle Management (ALM) process for Microsoft Foundry agent deployment, you must integrate Foundry Control Plane Observability into your CI/CD pipeline.
This ensures the agent is validated against baseline metrics before reaching production and monitored for drift thereafter.
Pre-Deployment: Baseline Evaluation
Before a new agent version is deployed, it must pass a rigorous evaluation stage within your ALM pipeline to prevent performance regressions.
Post-Deployment: Drift & Continuous Monitoring
Once deployed, the agent's performance must be continuously validated against the pre- deployment baseline using Observability in the Control Plane.
Continuous Evaluation: Configure the Control Plane to sample live production traffic and run the same evaluators used during development.
Drift Detection: Monitor for changes in Quality Scores and Accuracy Metrics over time. Significant drops indicate "drift," where real-world usage differs from your training/testing assumptions.
Unified Dashboard: Use the Foundry Monitoring Dashboard to visualize trends in latency, token usage, and accuracy side-by-side.
Alerting: Set up Azure Monitor alerts to notify developers immediately if evaluation scores fall below the established baseline.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/building-production-ready-secure-observable-ai-agents-with-real-time-voice-with-/4501074
質問 # 70
A company has a Microsoft Copilot Studio agent that provides answers based on a knowledge base for customer support.
Users report that, occasionally, the agent provides inaccurate answers.
You need to use metrics from the Analytics tab in Copilot Studio to identify the cause of the inaccuracies.
Which two options should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：A、E
解説：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are B. session information and session outcomes and E. quality of generated answers .
This scenario is focused on a knowledge base-driven Copilot Studio agent where users report that the agent sometimes gives inaccurate answers . The question asks which Analytics tab metrics should be used to identify the cause of those inaccuracies.
That means you need metrics that help you examine:
* how the answer was generated
* what happened in the conversation when the bad answer occurred
Why E. quality of generated answers is correct
This is the most direct metric for this scenario.
Because the agent is answering from a knowledge base , the problem is tied to the quality of the generated response itself. The quality of generated answers metric helps assess whether the generated responses are relevant, useful, and accurate enough for the user's request.
From an AI business solutions perspective, this metric is essential because it helps diagnose problems such as:
* weak grounding from the knowledge source
* irrelevant retrieval
* poor answer formulation
* hallucination-like behavior
* mismatch between user question and available source content
If the issue is inaccurate answers, the first place to investigate is the quality signal tied to generated answers.
Why B. session information and session outcomes is correct
To find the cause of inaccuracies, you also need to inspect the broader conversational context. Session information and session outcomes help you see:
* what the user asked
* how the agent responded
* whether the conversation was resolved
* whether the user abandoned, escalated, or retried
* where the conversation broke down
This is important because an inaccurate answer may not come only from poor generation quality. It may also come from:
* the way the user phrased the request
* lack of sufficient grounding context
* repeated failed attempts in a session
* escalation after an unhelpful answer
* patterns in unsuccessful conversations
In other words, quality of generated answers tells you about answer quality, while session information and outcomes help you understand the operational context in which those inaccuracies appear.
Together, these two give the strongest diagnostic view.
Why the other options are incorrect
A). survey results
Survey results can tell you whether users were happy or unhappy, but they do not directly help identify the cause of inaccurate knowledge-based responses. They are more of a feedback signal than a root-cause metric.
C). topic usage and topics with low resolution
This is more relevant for agents built around explicit topics and topic flows. The scenario specifically describes an agent that provides answers based on a knowledge base , so generated-answer analytics are more appropriate than topic-resolution analysis.
D). engagement, resolution, and escalation rates
These are useful high-level operational KPIs, but they are not the best metrics for diagnosing why answers are inaccurate. They show outcome trends, not the direct cause of answer-quality issues.
質問 # 71
A company uses multiple Microsoft Copilot Studio agents across different channels.
You need to recommend a monitoring solution that provides comprehensive telemetry data and performance insights for the agents.
What should you include in the recommendation?
正解：B
解説：
To achieve comprehensive monitoring, telemetry, and performance insights for Microsoft Copilot Studio agents across different channels, you should integrate them with Azure Application Insights.
Integrating Application Insights provides a centralized view of agent health, user interactions, topic performance, and latency, which is crucial for monitoring multi-channel deployments.
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/agents-view
質問 # 72
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.
Which template should you use for the AI agent to meet the requirements for the sales executives?
正解：A
解説：
Scenario:
Sales Cycle Enablement
To achieve the company's objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:
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.
To enable handsfree headset interaction with an AI agent for internal policies and customer data in Dynamics 365 Sales, you should use Microsoft Copilot Studio to create a custom agent template.
Here is the breakdown of the recommended approach and templates:
Recommended Template
Voice-enabled agent template (in Copilot Studio): This template provides the foundational, pre- configured setup for Interactive Voice Response (IVR) capabilities, allowing for natural language voice inputs and text-to-speech output.
Internal Data Knowledge Source: Within this agent, you will connect to Dataverse (for customer data) and configure Knowledge Sources (for internal policy documents).
Note:
To implement a hands-free AI agent for Dynamics 365 Sales using the Voice agent template in Microsoft Copilot Studio, follow these steps to enable voice-first interactions for internal policies and customer data.
1. Create a Voice-Enabled Agent
2. Configure Hands-Free Interaction
3. Connect to Internal Data
Reference:
https://learn.microsoft.com/en-us/dynamics365/contact-center/administer/bot-scenario-configure
質問 # 73
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AB-100勉強の資料: https://www.jpntest.com/shiken/AB-100-mondaishu
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