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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Exam Format: | Scenario-based, Multiple choice |
| Exam Price: | USD $85 |
| Related Certifications: | Microsoft Applied Skills |
| Passing Score: | 700 (out of 1000) |
| Certificate Validity Period: | 1 year (for recertification) |
| Real Exam Qty: | Around 40-50 questions |
| Available Languages: | English |
| Exam Duration: | 50 minutes |
| 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 # 46
A company has an AI agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
Answer: D
Explanation:
To automate feedback assessment and monthly reporting, you can build an agent flow in Microsoft Copilot Studio that functions as a "data pre-processor" and "insight generator." By using Agent Flows, you can move beyond simple Q&A to create structured, multi-step processes that clean raw data before it is ingested by the agent's main knowledge base.
Step 1: Design the Feedback Agent Flow
The "Flow" acts as the bridge between your cloud database (e.g., Azure SQL, Cosmos DB) and the final report.
Step 2: Ensure Data Quality (The "Clean Room")
For the agent to give accurate insights for marketing, the data must be grounded and structured.
Step 3: Automate the Monthly Report
Once the data is clean, the agent can summarize it into a report suitable for stakeholders.
Reference:
https://cps.co.uk/insights/how-to-use-copilot-studio-to-automate-repetitive-tasks
NEW QUESTION # 47
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 # 48
Which tools should you recommend to assist the CISO and the CIO with their specific responsibilities? To answer, drag the appropriate tools to the correct executives. Each tool 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.
Answer:
Explanation:
Explanation:
CISO # Azure Resource Graph Explorer
CIO # Microsoft Purview
Comprehensive and Detailed Explanation from Agentic AI Topics:
The CISO is responsible for discovering and inventorying AI resources for auditing across multiple Azure subscriptions. The best tool for that is Azure Resource Graph Explorer, because it is designed to query Azure resources at scale across subscriptions and provide visibility into what AI-related resources exist for governance and audit purposes.
The CIO is responsible for ensuring that appropriate security labels are assigned to the data used by the AI agents. The best tool for that is Microsoft Purview, because Purview supports data governance, classification, sensitivity labeling, and compliance management across enterprise data assets.
Why the other options are not correct:
Azure Blob Storage is storage, not the primary tool for discovery/inventory or labeling governance.
Copilot Studio is for building and managing agents, not for Azure-wide AI resource inventory or enterprise data labeling.
NEW QUESTION # 49
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 existing tool and data should you use to gather the required metrics for stakeholder signoff for the AI agents? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario:
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off.
Box 1: Microsoft Foundry
Tool
To secure stakeholder sign-off for a Microsoft AI solution using AI agents and Dataverse, Azure AI Foundry (formerly Azure AI Studio) provides a structured framework to gather metrics across five key "ROI levers":
1. Identify Key ROI Metrics via Foundry
Use the Foundry ROI Framework to categorize and collect data that compares the current manual process to the future AI-driven state.
2. Gather Data using Foundry Tools
Foundry offers specific technical features to generate the evidence needed for your business case.
3. Calculate the ROI
Once metrics are gathered, apply the standard Microsoft-recommended formula for the business case.
Box 2: the current time to complete the task today per instance
Data required for the tool
In the Microsoft Foundry ROI framework, the metric for current time required to complete a task today is typically referred to as the Current Time per Instance or Task Duration (Baseline). This value serves as the primary "Before" benchmark to calculate the efficiency gains of the new AI agent solution.
Reference:
https://itnext.io/stop-wasting-tokens-how-to-design-high-roi-ai-apps-on-microsoft-foundry-616622ddead6
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/a-framework-for-calculating-roi-for-agentic-ai-apps/4369169
NEW QUESTION # 50
You are designing end-to-end test scenarios for a business solution that uses Microsoft Dynamics 365 Sales and Dynamics 365 Finance. You need to ensure that the business solution meets the following test requirements:
* Properly exchanges data between the Dynamics 365 apps
* Aligns with defined user workflows and business processes
Which type of testing should you use for each requirement? To answer, drag the appropriate testing types to the correct requirements. Each testing 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.
Answer:
Explanation:
Explanation:
For end-to-end validation of a solution that uses Dynamics 365 Sales and Dynamics 365 Finance, the testing type should match the goal of each requirement.
For properly exchanging data between the Dynamics 365 apps, the correct testing type is Integration testing.
This verifies that the systems connect correctly, pass data accurately, and maintain consistency across app boundaries.
For aligning with defined user workflows and business processes, the correct testing type is User acceptance testing. This focuses on whether the solution supports real business tasks the way users expect and whether it fits the intended operational process.
Why the other options are not correct here:
Drift is about changes over time, often in model or behavior consistency.
Exploratory is useful for uncovering unexpected issues, but it is not the primary match for the stated requirement.
Performance focuses on speed, scale, and responsiveness, not workflow fit.
NEW QUESTION # 51
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