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Customizable Agentic AI Business Solutions Architect (AB-100) practice exams allow you to adjust the time and Microsoft AB-100 questions numbers according to your practice needs. Scenarios of our AB-100 Practice Tests are similar to the actual AB-100 exam. You feel like sitting in the real AB-100 exam while taking these AB-100 practice exams.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Related Certifications: | Microsoft Certified: Dynamics 365 Microsoft Certified: Power Platform Microsoft Certified: Azure AI Fundamentals |
| Available Languages: | English |
| Certificate Validity Period: | 12 months |
| Exam Duration: | 100 minutes |
| Exam Format: | Interactive question types, Multiple choice, Case studies, Scenario-based questions |
| Recommended Training: | Azure AI Foundry Documentation Microsoft Copilot Studio Learning Path |
| 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 # 105
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?
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics Designing an ALM process for fine #tuned Microsoft Foundry models requires two critical capabilities:
* Version-controlled training data
* A consistent, governed pipeline for retraining
Let's break down the reasoning using modern Agentic AI lifecycle , data governance , and model retraining best practices .
E). Store the training data in Azure Blob Storage that has version control enabled - # Correct This directly satisfies the requirement:
"Data changes must be tracked and versioned."
Azure Blob Storage with versioning provides:
* Automatic version history for every training dataset
* Immutable snapshots for audit and rollback
* Governance controls for approved data
* Integration with CI/CD pipelines for model retraining
In an agentic AI lifecycle, data versioning is mandatory because:
* Training data evolves frequently
* Retraining must be reproducible
* Regulatory audits require traceability
* Model drift must be monitored
Blob Storage with versioning is the Microsoft#recommended approach for enterprise AI ALM.
D). Upload the training data to Microsoft Foundry data files - # Correct Foundry fine #tuning jobs require training data to be stored in Foundry data files .
This ensures:
* The fine #tuning job always uses the approved dataset
* The model retraining pipeline is consistent
* The data is validated and formatted correctly
* The training job references a stable, governed data source
This aligns with the requirement:
"The model must be retrained consistently by using approved training data." In agentic AI systems, the training pipeline must be deterministic.
Uploading the data to Foundry data files ensures that the fine#tuning job always uses the correct dataset version.
# Why the other options are NOT correct
A). Associate the storage location to the fine-tuning job - Not sufficient This does not provide:
* Data versioning
* Governance
* Tracking of changes
It simply points the job to a location, not a controlled ALM process.
B). Create a content filter - Not related to ALM or training data
Content filters are for safety , not:
* Versioning
* Data governance
* Retraining consistency
They do not help with the ALM requirements.
C). Store the training data in Azure Files - Not appropriate
Azure Files does not provide:
* Built#in versioning
* Immutable snapshots
* ALM integration for ML pipelines
Blob Storage is the correct choice for AI training data.
Final Answer: D, E
* D. Upload the training data to Microsoft Foundry data files
* E. Store the training data in Azure Blob Storage that has version control enabled These two actions together create a governed, versioned, repeatable ALM pipeline for fine #tuned Foundry models
NEW QUESTION # 106
A company uses Azure OpenAI models that use grounding data from Microsoft Fabric for agents. The models are fine-tuned by using proprietary datasets.
You need to design a governance solution that meets the following requirements:
Restricts access to the grounding data to only assigned roles
Restricts model fine-tuning to only the AI engineering team
What should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Restricts access to grounding data # Microsoft Purview access policies; Restricts model fine-tuning # Role- based access control (RBAC) in Microsoft Foundry Why Microsoft Purview access policies is correct The grounding data is stored in Microsoft Fabric, and the requirement is to restrict access to that data to only assigned roles.
That is a data governance and access control requirement. Microsoft Purview access policies are the best fit because they are designed to govern and control access to data across enterprise data estates. In this case, they help ensure that only authorized roles can access the grounding data used by the agents.
From an AI business solutions perspective, grounding data is often one of the most sensitive parts of the solution because it can contain:
proprietary business knowledge
internal documents
regulated operational information
contextual data used to shape model outputs
Purview helps enforce governed access to that data layer rather than relying only on general infrastructure controls.
Why RBAC in Microsoft Foundry is correct
The second requirement is to ensure that only the AI engineering team can perform model fine-tuning.
That is an action-level platform permission requirement. The best control for that is role-based access control (RBAC) in Microsoft Foundry.
RBAC allows the organization to assign permissions based on job function, so only authorized users or groups can:
create or modify fine-tuning jobs
manage model assets
update training configurations
control deployment-related AI resources
This is the right governance pattern because fine-tuning changes model behavior and can introduce:
security risk
compliance risk
quality drift
misuse of proprietary datasets
Restricting that capability to the AI engineering team through RBAC creates a clear separation of duties.
Why the other options are incorrect
Azure AI Content Safety
This is used to detect and filter harmful content. It does not control access to Fabric grounding data.
Azure Monitor alerts
Alerts help observe activity, but they do not enforce role-based access to data.
Azure Policy compliance rules
Azure Policy is useful for enforcing resource configuration standards, but it is not the best answer for role- based access to Fabric grounding data or for limiting fine-tuning actions to a specific team.
Azure Resource Manager (ARM) resource locks
Resource locks help prevent deletion or modification of Azure resources, but they do not provide the right permission model for controlling who can perform model fine-tuning operations.
Microsoft Entra Conditional Access
Conditional Access is mainly about sign-in and access conditions, such as device, location, or risk context. It is not the best direct control for restricting fine-tuning permissions inside Foundry.
Expert reasoning
Use this exam shortcut:
Need to control access to enterprise data # think Purview access policies Need to restrict who can perform AI platform actions like fine-tuning # think RBAC in the AI platform So the correct mapping is:
Restricts access to the grounding data: Microsoft Purview access policies Restricts model fine-tuning: Role-based access control (RBAC) in Microsoft Foundry
NEW QUESTION # 107
You are designing an AI strategy for Microsoft Dynamics 365 finance and operations apps. You are evaluating the use of Microsoft Copilot Studio to provide in-app help and guidance based on generative AI general knowledge.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Custom knowledge sources # Must be uploaded to the agent; AI general knowledge # Must be disabled for the agent The requirement says the solution must minimize the risk of generating inaccurate responses. In generative AI business solutions, the best way to reduce hallucinations or loosely grounded answers is to rely on approved, scoped, domain-specific knowledge rather than broad general knowledge.
That means the agent should use:
custom knowledge sources that are intentionally provided to the agent
AI general knowledge disabled, so the agent does not answer from broad open-ended model knowledge when enterprise-specific guidance is needed In finance and operations scenarios, this is especially important because users may ask about:
internal business processes
financial controls
operational procedures
policy-specific guidance
company-configured ERP workflows
These are areas where inaccurate answers can create operational or compliance risk. Grounding the agent on uploaded custom sources provides a more controlled knowledge base.
Why custom knowledge sources must be uploaded
If the goal is in-app help and guidance for Dynamics 365 finance and operations apps, the safest and most reliable approach is to provide the agent with curated documentation such as:
internal process guides
approved SOPs
finance workflow documentation
policy documents
ERP-specific instructions
Uploading these custom knowledge sources ensures the agent answers from enterprise-approved material rather than from generic model knowledge.
From an AI governance perspective, this improves:
factual grounding
relevance
auditability
trustworthiness
alignment to company-specific processes
Why AI general knowledge must be disabled
General AI knowledge can be useful in broad assistant scenarios, but in a finance and operations help context it increases the risk that the agent may generate:
overly generic responses
answers not aligned to company policy
incorrect interpretations of internal process steps
content that sounds plausible but is not operationally correct
Because the question explicitly says to minimize the risk of inaccurate responses, the safer design is to disable general knowledge and constrain responses to approved custom sources.
NEW QUESTION # 108
What should you recommend to assist the CTO with the prebuilt agent selection process?
Answer: B
Explanation:
The CTO wants to view available prebuilt agent templates in Dynamics 365 Supply Chain Management to decide which one should be prioritized for deployment. Agent management is the feature used to discover, review, and manage available agent templates and capabilities for Dynamics 365 business applications.
Why this is correct:
It supports discovering available prebuilt agents
It helps evaluate which agent can deliver the most business value
It aligns with the requirement to preview and assess candidate agents during the selection phase Why the other options are not correct:
B). Immersive Home is more of an experience surface, not the primary tool for selecting prebuilt agent templates C). Lifecycle Services (LCS) is used for environment and application lifecycle management, not for browsing Dynamics 365 AI agent templates D). Copilot Studio is primarily for building/customizing copilots and agents, not for selecting Dynamics 365 prebuilt Supply Chain Management agent templates
NEW QUESTION # 109
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 # 110
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