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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Available Languages: | English |
| Exam Duration: | 100 minutes |
| Exam Format: | Scenario-based questions, Multiple choice, Interactive question types, Case studies |
| Certificate Validity Period: | 12 months |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals Microsoft Certified: Power Platform Microsoft Certified: Dynamics 365 |
| Recommended Training: | Microsoft Copilot Studio Learning Path Azure AI Foundry Documentation |
| Exam Registration: | Microsoft Learn Certification Page AB-100 Exam Study Guide |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored exam (with possible interactive/case-based components) |
| Pre Condition: | Recommended: Active Microsoft Associate-level certification in AI/Power Platform/Dynamics 365 (e.g., AI-102, PL-600, MB-910 or similar) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-business-solutions-architect/ |
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NEW QUESTION # 15
A company has a Microsoft Power Platform solution that contains the following components:
- Microsoft Dataverse tables
- A Microsoft Power BI workspace named WS1
- A canvas app named App1 that uses Dataverse
- A Power BI semantic model that connects to Dataverse by using
DirectQuery
You plan to use generative AI to provide answers to queries based on a subset of corporate data.
You need to ensure that the data is available as a grounding data source for AI systems.
What should you do?
Answer: B
Explanation:
Consider using tags to label semantic models as ready for Copilot consumption. This method helps data consumers identify models that they can use with Copilot and expect better results.
Alternatively, consider the readiness of a semantic model for use with Copilot as a criteria for its endorsement to promoted or even certified status.
Reference:
https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-semantic-models
NEW QUESTION # 16
What should you include in the custom Al agent design to meet the R & D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To expose the data to the agent, create: a custom connector
Add to the agent: the MCP server
The custom agent must answer questions about product specifications and compliance information, and the case study states that the R & D department already has a custom Model Context Protocol (MCP) server containing that information.
The best design is:
create a custom connector to expose that external capability cleanly to the low-code Copilot solution add the MCP server to the agent so the agent can use that external knowledge/tooling source Why this is correct:
The MCP server already exists and contains the needed product-specification and compliance data.
In a Copilot/agent design, you need a way to expose external functionality and data in a reusable, secure way.
A custom connector is the low-code integration mechanism that fits this requirement.
Then the agent can use the MCP server as the connected external capability for answering those questions.
Why the other options are not correct:
Azure AI Bot Service channel is for communication channels, not for exposing this knowledge source.
a custom OData entity is not the right pattern for integrating the existing MCP-based capability.
the Semantic Kernel is a developer orchestration framework, but the requirement emphasizes using the existing MCP technology in a low-code solution.
an event trigger is unrelated to exposing R & D specification/compliance knowledge.
a REST API is too generic here; the scenario specifically points to the existing MCP server.
a tool is close conceptually, but the most direct answer choice tied to the existing R & D technology is the MCP server.
NEW QUESTION # 17
A company uses Microsoft Dynamics 365 finance and operations apps.
The company plans to use Microsoft Copilot in-app help and guidance to generate responses for internal business processes.
You need to add an additional knowledge source for the business processes. The solution must NOT add new topics to the Copilot agent for the finance and operations apps.
Which knowledge source should you add?
Answer: C
Explanation:
To add an additional knowledge source for internal business processes to the Microsoft Copilot in-app experience for Dynamics 365 finance and operations apps-without creating new topics- you should add File Uploads (such as PDF, Word, or text documents) to the "Copilot for finance and operations apps" agent in Copilot Studio.
Reference:
https://learn.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/copilot/extend-copilot- generative-help
NEW QUESTION # 18
A company has multiple AI models that support generation of sales transactions.
Each release of the models must be reviewed by a security and compliance team before being deployed to the production environment. The security and compliance team must have access to prior versions to properly determine potential exposures introduced.
You need to recommend a solution to evaluate the impact of each deployment to production. The solution must enhance business continuity.
What should you recommend?
Answer: C
Explanation:
To ensure business continuity and minimize risks in AI-driven sales transaction systems, implementing comprehensive version control across all system components is a critical requirement. This provides reviewers with a stable baseline to evaluate new releases against older versions, helping identify potential exposures or regressions before they reach production.
Strategic Implementation for AI Version Control
Version All Components: Do not limit version control to application code. You must track:
*-> Models: Managed iterations including weights and architecture.
Etc.
Benefits for Business Continuity
Predictability: Standardized versioning makes AI behavior more auditable and scalable.
Disaster Recovery: Allows teams to quickly reproduce or restore any previous environment state during a failure.
Regulatory Compliance: Provides the necessary evidence of "what the AI was instructed to do" at any given point, which is mandatory for regulated financial environments.
Reference:
https://www.kore.ai/blog/why-prompt-version-control-matters-in-agent-development
NEW QUESTION # 19
A company uses a fine-tuned Microsoft Foundry model that requires frequent updates as new customer feedback becomes available.
You need to design an application lifecycle management (ALM) process that meets the following requirements:
- Data changes must be tracked and versioned.
- The model must be retrained consistently by using approved training
data.
Which two actions should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
To implement a new Application Lifecycle Management (ALM) process for your fine-tuned Microsoft Foundry model, you can leverage Azure Blob Storage for raw data versioning and Azure AI Foundry (formerly Azure AI Studio) for managed data assets and model retraining.
1. Data Tracking and Versioning in Azure Blob Storage
Enable blob versioning to automatically maintain a history of your training data.
Enable Versioning: In the Azure portal, navigate to your storage account under Data management > Data protection and select Enable versioning for blobs.
Immutable Tracking: This ensures that every update to a training file creates a new version, allowing you to restore or reference specific historical states of your dataset for auditability.
2. Uploading and Managing Data in Microsoft Foundry
Integrate your versioned blob storage with Microsoft Foundry to create "Data assets" that the model can use for retraining.
Reference:
https://learn.microsoft.com/en-us/azure/storage/blobs/versioning-enable
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/data-add?view=foundry-classic
NEW QUESTION # 20
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