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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Exam Price: | USD $85 |
| Certificate Validity Period: | 1 year (for recertification) |
| Passing Score: | 700 (out of 1000) |
| Exam Duration: | 50 minutes |
| Related Certifications: | Microsoft Applied Skills |
| Exam Format: | Scenario-based, Multiple choice |
| Available Languages: | English |
| 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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| 주제 | 소개 |
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| 주제 3 |
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질문 # 18
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.
정답：
설명：
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 produc tion 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 d eployment: 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 cr edentials 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 wou ld 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
질문 # 19
You are designing a testing solution for a Microsoft Copilot Studio agent that integrates with Microsoft Dynamics 365 Customer Service and Dynamics 365 Sales.
You need to design end-to-end scenarios to test the agent ' s ability to perform the following actions:
Coordinate tasks and data interactions across both Dynamics 365 apps.
Interpret user input and provide contextually relevant outputs.
Which test scenario and metric should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Test scenario # Run task-based scenarios that involve both apps; Metric # Track the successful completion of cross-app tasks Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is D:
Test scenario # Run task-based scenarios that involve both apps
Metric # Track the successful completion of cross-app tasks
Why this test scenario is correct
The question explicitly asks for an end-to-end testing design for a Copilot Studio agent that integrates with both:
Dynamics 365 Customer Service
Dynamics 365 Sales
The required capabilities are:
coordinating tasks and data interactions across both apps
interpreting user input and returning contextually relevant outputs
That means the testing approach cannot be isolated to one app at a time. It must validate the agent's behavior across the full multi-application business workflow.
That is why the correct test scenario is:
Run task-based scenarios that involve both apps
This kind of test validates whether the agent can successfully move through realistic business processes such as:
reading customer context from one app
updating or retrieving related sales information in the other
maintaining context through the workflow
responding appropriately based on user intent across systems
From an agentic AI business solutions perspective, this is the right design because true enterprise agent validation must focus on workflow execution, not just component-level checks.
Why this metric is correct
The best metric is:
Track the successful completion of cross-app tasks
This is the most direct way to measure whether the agent is actually achieving the intended business outcome across both Dynamics 365 applications.
Why this matters:
The requirement is about coordination across apps
The test is end-to-end
The goal is not just speed or UI consistency
The agent must complete business tasks successfully across systems
A cross-app completion metric shows whether the agent can:
understand the user's request
maintain context
retrieve or update the right information
finish the workflow correctly across app boundaries
This is much more meaningful than measuring clicks or simple response time.
Why the other options are incorrect
A). In each app, test isolated tasks without using workflows / Measure initial prompt response time This fails the end-to-end requirement. Isolated tasks do not validate cross-app orchestration, and response time does not prove successful workflow execution.
B). Run task-based scenarios that involve both apps / Track average click rate across both apps The scenario part is good, but average click rate is not the right success metric for Copilot task orchestration.
Clicks do not reliably measure whether the business process was completed correctly.
C). Test visual consistency across both apps / Track successful completion of cross-app tasks The metric is good, but the test scenario is wrong. Visual consistency is a UI concern, not an end-to-end functional validation of cross-app agent behavior.
Expert reasoning
For exam questions like this:
If the requirement says end-to-end across multiple apps, choose task-based scenarios involving both apps If the goal is business workflow success, choose a metric tied to task completion, not visual design, click rate, or raw response speed
질문 # 20
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 tool should you recommend to help secure funding for future AI agent development?
정답：C
설명：
Scenario
Requirements
Any created AI agents must have their return on investment (ROI) calculated to ensure that the solution will save the company money.
--
In a Microsoft AI migration, calculating the Return on Investment (ROI) for AI agents is essential for justifying costs and securing future funding. You can utilize the Analytics tab in Microsoft Copilot Studio to track these financial and performance metrics directly.
Calculating ROI with Copilot Studio Analytics
The Analytics tab provides a dedicated Savings tile (ROI) that allows you to quantify the impact of your agents:
Define Savings Parameters: You can input estimated time saved (in seconds, minutes, or hours) and money saved per successful agent run.
Real-Time Tracking: Total savings are calculated automatically for your selected period based on successful runs of resolved conversations.
Retroactive Application: If you update your savings estimates, the system can apply these changes to previous runs to provide an accurate historical view.
Granular Insights: Savings can be defined at the overall agent-run level or for specific tools used within a run.
Reference:
https://learn.microsoft.com/en-us/training/modules/forecast-agent-return-investment/
질문 # 21
Drag and Drop Question
A company plans to implement an AI business solution for a consumer goods company.
You need to create agents that meet the following requirements:
- Orchestrate the sales order fulfillment and shipping of goods to
customers.
- Analyze historical data and trends to replenish stock.
Which type of agent should you use for each requirement? To answer, drag the appropriate agent types to the correct requirements. Each agent type 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.
정답：
설명：
Explanation:
Box 1: Autonomous
This requirement involves complex, multi-step workflows that must happen independently across multiple systems (e.g., triggering fulfillment when an order is received). Autonomous agents are specifically designed to perceive events, make decisions, and execute sequences of actions - such as placing orders or updating records - without waiting for a direct user prompt.
Box 2: Task
This requirement describes a specialized, assisted automation role focused on a specific objective - processing and summarizing existing corporate data to produce an actionable output.
Task agents (often referred to as specialized Copilot agents) excel at these defined scenarios, such as demand forecasting or inventory health checks, where they reason through data within set boundaries to help move work forward.
질문 # 22
Hotspot Question
A company has a Microsoft Copilot Studio agent for customer support.
You are reviewing and validating the following prompts:
- A prompt that has instructions to "help the customer as best you can"
- A prompt that helps retrieve product information from a knowledge
base
You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 23
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