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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Available Languages: | English |
| Related Certifications: | Microsoft Applied Skills |
| Exam Duration: | 50 minutes |
| Exam Format: | Multiple choice, Scenario-based |
| Certificate Validity Period: | 1 year (for recertification) |
| Exam Price: | USD $85 |
| Passing Score: | 700 (out of 1000) |
| Real Exam Qty: | Around 40-50 questions |
| 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/ |
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NEW QUESTION # 42
A company has an AI solution that uses a Microsoft Copilot Studio agent.
You need to monitor the agent ' s performance. The solution must meet the following requirements:
Monitor the agent ' s telemetry in near-real-time (NRT).
Download transcripts of full conversations.
Monitor the agent ' s usage and performance.
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.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Monitor telemetry in NRT # Application Insights; Download transcripts of full conversations # Copilot Studio; Monitor usage and performance # Copilot Studio The correct mapping is:
* Monitor the agent ' s telemetry in near-real-time (NRT) # Application Insights
* Download transcripts of full conversations # Copilot Studio
* Monitor the agent ' s usage and performance # Copilot Studio
Why Application Insights is correct for NRT telemetry
Application Insights is the right choice for near-real-time telemetry because it is built for operational monitoring of application events, traces, failures, latency, and runtime behavior.
For a Copilot Studio agent, Application Insights is used when you want fast visibility into:
* request activity
* errors and exceptions
* latency trends
* runtime traces
* telemetry streaming for troubleshooting
From an AI business solutions perspective, this is essential for early detection of issues in production, especially when the agent supports customer or employee workflows.
Why Copilot Studio is correct for full conversation transcripts
Copilot Studio provides access to conversation/session-level analysis, including the ability to review and download full conversation transcripts.
This is the correct place to inspect:
* what the user asked
* how the agent responded
* where the conversation failed
* whether escalation occurred
* what happened across the whole interaction
That makes it the best tool for transcript retrieval and investigation.
Why Copilot Studio is also correct for usage and performance
Copilot Studio includes built-in analytics for monitoring agent usage and performance, such as:
* conversation volume
* engagement
* resolution trends
* escalation behavior
* answer quality and outcomes
This supports business-level performance monitoring and continuous improvement.
Why the other options are incorrect
Log Analytics
Log Analytics is useful for querying centralized logs, but it is not the best direct answer here for near-real- time Copilot telemetry or full conversation transcript download in this scenario.
Microsoft Power Apps
Power Apps is not the monitoring platform for Copilot Studio telemetry, transcripts, or usage/performance analytics.
Expert reasoning
Use this exam shortcut:
* Near-real-time telemetry and operational tracing # Application Insights
* Conversation transcripts # Copilot Studio
* Agent usage and performance analytics # Copilot Studio
NEW QUESTION # 43
What should you configure for the custom Al agent?
Answer: B
Explanation:
The custom AI agent must select topics based on a description of the purpose of the query, rather than relying on fixed trigger phrases. That is exactly the scenario where generative orchestration is the right configuration.
Why B is correct:
It enables the agent to choose the best topic, tool, or action based on the semantic meaning of the user's request It supports more conversational interactions It aligns directly with the requirement that topic selection should not depend on trigger phrases Why the other options are not correct:
A). Azure OpenAI reasoning models concerns model capability, not topic-routing behavior in Copilot Studio C). classic orchestration relies more on predefined topics and trigger phrases D). AI-assisted evaluators are for assessment/testing, not runtime orchestration
NEW QUESTION # 44
A company uses Microsoft Dynamics 365 Finance to manage accounts payable.
You are designing an AI invoice processing solution.
You need to recommend the prerequisites to configure a prebuilt copilot for accounts payable.
What should you recommend?
Answer: A
Explanation:
Users must be assigned the Finance and Operations AI and AIB Roles in the Dataverse environment.
Note:
To configure the prebuilt Copilot for Accounts Payable in Microsoft Dynamics 365 Finance, you must satisfy several foundational and technical requirements.
1. System & Environment Prerequisites
2. Technical Configuration & Data Settings
3. Licensing & Security Roles
Licenses
*-> Dataverse Roles: Users must be assigned the Finance and Operations AI and AIB Roles in the Dataverse environment.
4. Feature Activation
Reference:
https://learn.microsoft.com/en-us/dynamics365/finance/accounts-payable/invoice-capture- overview
NEW QUESTION # 45
A company has Microsoft Power Platform development staging, and production environments. Each environment has its own Microsoft Dataverse tables and Azure Al Search index.
You are designing an application lifecycle management (ALM) process to deploy a Microsoft Copilot Studio agent between the environments.
The company has a Copilot Studio agent named Agent! in development. Agent1 uses the following grounding data sources:
* A Dataverse table named CustomerOrders
* An Azure Al Search index named customer-knowledge
You need to deploy Agent1 to production. The solution must ensure that the agent uses the production grounding data sources, minimizes downtime, and handles credentials and endpoints securely.
What should you include in the deployment package solution, and what should you reconfigure after the deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In a proper ALM deployment for Microsoft Copilot Studio across development, staging, and production, you should package the agent in a way that is portable across environments while avoiding hardcoded endpoints, indexes, table targets, or credentials.
Here, Agent1 uses:
a Dataverse table: CustomerOrders
an Azure AI Search index: customer-knowledge
Because each environment has its own Dataverse tables and Azure AI Search index, the deployment package should not carry over the development environment's live connections as fixed production settings. Instead, it should carry the agent and the references needed so the target environment can bind to its own production resources.
That is why the correct recommendation is:
Deployment package: Agent1 and references to the data sources
After deployment: Reconfigure the environment variables
Why this is correct:
Environment variables are the standard ALM-friendly way to externalize settings like:
endpoints
index names
table references
connection-related values
This supports secure handling of credentials and endpoints
It also helps minimize downtime, because production values can be switched cleanly after import without rebuilding the agent Why the other choices are weaker:
Agent1 only would omit needed source references
The data sources only would not deploy the actual agent
Agent1 and the data source connections risks carrying environment-specific connection bindings Agent1, the data sources, and the data source connections is too tightly coupled to the source environment and is not the best ALM design for secure cross-environment deployment Reconfiguring only Dataverse or only Azure AI Search is incomplete because both can vary by environment Reconfiguring Agent1 configuration is broader and less precise than using environment variables
NEW QUESTION # 46
A company processes invoices stored across multiple systems in multiple formats.
You need to implement an Al solution to automate the invoice processing. The solution must meet the following requirements:
* Automate multi-step invoice processing tasks, including document analysis, data validation, and approval routing.
* Enable users to interact directly via Microsoft Teams to review and approve invoices.
* Minimize development efforts to define and customize approval workflows.
What should you include in the solution?
Answer: D
Explanation:
This scenario requires an AI solution for invoice processing across multiple systems and formats , while also allowing direct user interaction in Microsoft Teams and keeping workflow customization effort low.
The best option is Microsoft Copilot Studio and AI Builder .
Why this is correct:
* AI Builder can be used for document-focused automation tasks such as extracting data from invoices and supporting structured business document processing.
* Microsoft Copilot Studio can provide the conversational and workflow-driven layer, including interaction through Microsoft Teams .
* Together, they support multi-step automation , including:
* document analysis
* extracted data handling
* validation steps
* routing for human review and approval
* This combination also aligns with the requirement to minimize development effort , because Copilot Studio and AI Builder are low-code tools designed for rapid business solution delivery.
Why the other options are less suitable:
* A. Azure Document Intelligence in Foundry Tools and Azure Logic Apps This can handle extraction and workflow automation, but it usually requires more implementation effort and does not as naturally satisfy the Teams-centric low-code user interaction requirement as Copilot Studio does.
* C. Azure OpenAI and Azure Functions This is far more custom-development-heavy and does not meet the goal of minimizing workflow customization effort.
* D. a SharePoint agent A SharePoint agent is too narrow for this broader multi-system invoice processing and approval workflow scenario.
NEW QUESTION # 47
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