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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Exam Format: | Multiple choice, Interactive items, Multiple response, Case studies, Scenario-based |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40โ60 |
| Exam Price: | $165 USD |
| Certificate Validity Period: | 1 year (annual renewal via free online assessment) |
| Related Certifications: | Microsoft Certified: Dynamics 365 Certified Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Power Platform Functional Consultant Associate |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | English |
| Recommended Training: | Official Microsoft Learn learning paths |
| Exam Registration: | Register via Microsoft Learn / Pearson VUE |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended: experience designing AI solutions, knowledge of Dynamics 365, Power Platform, Copilot Studio, Azure AI Foundry; no mandatory prerequisites |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-100 |
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NEW QUESTION # 28
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: B
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is C. Implement version control for all the AI system components .
This question is not only about model approval. It is about creating a deployment process that allows the organization to:
* review every release before production
* compare current and prior versions
* evaluate the impact of changes
* improve business continuity if a deployment introduces risk
That makes version control for all AI system components the strongest answer.
Why C is correct
The requirement says the security and compliance team must have access to prior versions to determine exposures introduced by each release. That means the organization must be able to track, compare, and potentially roll back not just the model itself, but the broader AI solution over time.
In real enterprise AI deployments, "AI system components" usually include:
* models
* prompts
* orchestration logic
* configuration files
* policies
* connectors
* inference code
* evaluation assets
* deployment definitions
If only the model is versioned, the team may miss exposure introduced by surrounding components. For example:
* a prompt change could create unsafe outputs
* a policy/configuration change could expose sensitive data
* an orchestration update could alter transaction behavior
* a connector change could affect compliance boundaries
That is why full AI system version control is the best answer. It gives security and compliance teams complete visibility into what changed across releases.
It also enhances business continuity because version control supports:
* rollback to known-good versions
* change auditing
* release comparison
* traceability
* controlled recovery from faulty deployments
From an agentic AI business solutions perspective, this is the most robust governance pattern because AI outcomes are rarely determined by the model alone. They are determined by the entire solution stack.
Why the other options are less appropriate
A). Create a central model registry that uses version history
A model registry is useful, and version history helps, but this option is too narrow. The question asks about evaluating the impact of each deployment and enhancing business continuity. In enterprise AI systems, impact is often caused by more than just the model artifact. A model registry does not necessarily capture all surrounding components that affect production behavior.
B). Establish a promotion process by using a quality gate
A quality gate is valuable for approval workflows, but it does not by itself satisfy the need for deep access to prior versions across the system. It controls promotion, but it does not fully provide historical traceability and rollback coverage for all AI system components.
D). Track model retirement schedules to prevent service disruptions
This may support lifecycle planning, but it does not address the core requirement of comparing releases, reviewing prior versions, and evaluating exposure introduced by each deployment.
Expert reasoning
This question combines three ideas:
* security/compliance review
* access to prior versions
* business continuity
When those appear together, the strongest answer is typically the one that provides end-to-end traceability and rollback across the whole solution , not just a single artifact.
That is why version control for all AI system components is the best recommendation.
NEW QUESTION # 29
Hotspot Question
You use Microsoft Copilot Studio analytics to analyze the performance of a deployed Copilot Studio agent.
You need to identify which performance metrics to use to measure the following:
- The percentage of engaged sessions that are escalated to a live
customer service representative
- The number of agent queries that cause a knowledge source error
What should you identify for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 30
A manufacturing company wants to deploy an agent that will automate supplier invoice processing.
You are designing a solution to evaluate the financial implications of the deployment. The company is especially concerned about budget overruns.
You need to ensure that the solution considers the total cost of ownership (TCO), the expected savings from using automation, and whether to extend the existing AI capabilities.
What should you include in the design?
Answer: C
Explanation:
This design plan outlines the financial and operational framework for deploying a Microsoft-based AI agent for supplier invoice processing. By leveraging current industry benchmarks and Microsoft's own agentic AI frameworks, this plan focuses on maximizing Return on AI Investment (ROAI) while mitigating the risk of budget overruns.
Reference:
https://www.investing.com/news/swot-analysis/microsofts-swot-analysis-cloud-and-ai-fuel-growth-but-challenges-loom-93CH-4197719
NEW QUESTION # 31
Hotspot Question
A company deploys agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
You need to recommend a governance solution that meets the following requirements:
- Enforces the deployment of the resources to only approved Azure
regions
- Provides continuous compliance verification of the resources
What should you include in the recommendation for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Azure Policy
Enforces the deployment of the resources to only approved Azure regions To enforce the deployment of Azure OpenAI resources to only approved Azure regions (e.g., specific regions in Europe and the USA), you should use Azure Policy with the "Allowed locations" policy definition.
Here is the breakdown of how to implement this control:
Primary Tool: Azure Policy
Azure Policy allows you to define rules that restrict where resources can be created.
Policy Rule: Use the Allowed locations policy definition.
Implementation: Assign this policy at the Subscription or Resource Group level to restrict developers to only using permitted regions (e.g., East US, West Europe).
Effect: If a user attempts to deploy an Azure OpenAI resource in a non-approved region, the deployment will be blocked.
Box 2: Microsoft Purview
Provides continuous compliance verification of the resources
To provide continuous compliance verification for Azure OpenAI resources across Europe and the USA, you should use Microsoft Purview Compliance Manager and Azure Policy.
Microsoft Purview Compliance Manager: This tool provides a risk-based compliance score and continuous monitoring against global regulations such as the EU AI Act, GDPR, and various US standards. It offers specific regulatory templates to help you assess and implement controls for generative AI applications.
Azure Policy: Use this to enforce organizational standards and assess compliance at scale. You can apply built-in policy definitions for Azure AI services to automatically audit or deny non- compliant resource configurations, such as ensuring resources are restricted to specific regions (e.g., only EU or USA) or have private network access enabled.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types
https://learn.microsoft.com/en-us/purview/ai-agent-365
NEW QUESTION # 32
A company plans to implement an Al 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.
Answer:
Explanation:
Explanation:
This question separates two different kinds of agent behavior.
For orchestrating sales order fulfillment and shipping, the best fit is an Autonomous agent. That requirement involves coordinating multiple steps, making decisions across a process, and driving execution across a workflow with limited manual intervention. Autonomous agents are designed for this kind of end-to-end orchestration.
For analyzing historical data and trends to replenish stock, the best fit is a Task agent. This requirement is more focused and bounded: analyze data, identify patterns, and support a specific business function. That aligns with a task-oriented agent rather than a broad orchestration agent.
Why Prompt-and-response is not the best answer here:
It is better suited for direct user query/answer interactions
It is not the strongest fit for process orchestration or structured business analysis workflows
NEW QUESTION # 33
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