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The web-based AB-100 practice test is accessible via any browser. This AB-100 mock exam simulates the actual Agentic AI Business Solutions Architect (AB-100) exam and does not require any software or plugins. Compatible with iOS, Mac, Android, and Windows operating systems, it provides all the features of the desktop-based AB-100 Practice Exam software.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan AI-powered business solutions | 25% | - AI solution design strategy
|
| Topic 2: Deploy AI-powered business solutions | 45% | - Security, governance, and ALM
|
| Topic 3: Design AI-powered business solutions | 30% | - Extensibility and orchestration
|
>> Reliable AB-100 Exam Topics <<
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NEW QUESTION # 38
A company has an Azure environment that supports multiple business units.
The company plans to implement an AI solution that will perform sentiment analysis on customer product reviews.
You need to evaluate the potential cost of the solution to support return on AI investment (ROAI) analysis.
What should you use?
Answer: C
Explanation:
Correct:
* Azure pricing calculator (Best choice)
The best tool to evaluate the potential cost of a new AI solution to support ROI analysis is the Azure pricing calculator.
Why Azure Pricing Calculator is BestProactive Estimation: It is specifically designed to estimate the costs of Azure services before they are deployed.
Granular Planning: It allows you to select specific services (e.g., Azure AI Language, Cognitive Services, Azure Machine Learning), define usage patterns, regions, and tiers to create a detailed cost estimate.
Scenario Comparison: You can create different scenarios (e.g., comparing different language models or usage volumes) to estimate the impact on ROAI Comparison of Tools Azure Pricing Calculator: Estimates costs for new, un-deployed projects. (Best Choice).
Total Cost of Ownership (TCO) Calculator: Compares on-premises infrastructure costs against Azure, typically used for large-scale migration strategies, not specific AI service modeling.Cost Management + Billing: Monitors and analyzes actual, real-time spending on already deployed resources. For a complex AI solution involving.
* Total Cost of Ownership (TCO) Calculator (Second best choice)
While Cost Management + Billing is essential for tracking actual spend once a solution is running, the TCO Calculator is specifically designed for the pre-implementation phase to support Return on AI Investment (ROAI) analysis. It allows you to estimate and compare the potential costs of moving a workload to Azure-including labor, networking, and storage-against an existing process or alternative solution.
* Cost Management + Billing (Third best)
Azure pricing calculator and Total Cost of Ownership (TCO) Calculator are both better than Cost Management + Billing, as they evaluate the potential cost and support a Return on AI Investment (ROAI) analysis before the solution is built, you need to estimate future expenses based on expected usage. Cost Management + Billing does post-deployment tracking.
A good tool still for this scenario is Cost Management + Billing. Cost Management + Billing allows you to break down and evaluate costs across many business units using tags and scope filtering.
This granular data is essential for calculating the Return on AI Investment (ROAI) for specific departments or the entire organization.
Incorrect:
* Azure Monitor
* Azure Reservations
* Azure savings plans
Is used to reduce costs for compute services via long-term commitment. It is a way to lower your bill, but not a tool for evaluating or reporting on investment performance.
* Microsoft Fabric SKU Estimator
Is used to estimate capacity needs for Fabric-specific data projects. It is eseful only if your sentiment analysis solution is built specifically on the Microsoft Fabric platform.
Note:
* Azure pricing calculator
To evaluate the potential cost of an AI solution for sentiment analysis on Azure, you should use the Azure Pricing Calculator.This tool allows you to estimate the monthly and annual costs of the specific services required for your solution-such as Azure AI Language (which provides sentiment analysis) or Azure OpenAI Service-based on your expected volume of feedback data.
* Total Cost of Ownership (TCO) Calculator
To evaluate the cost of an Azure sentiment analysis solution and support a Return on AI Investment (ROAI) analysis for multiple business units, you should use the Azure Total Cost of Ownership (TCO) Calculator.
The TCO Calculator is essential for calculating the financial impact of migrating or implementing a new solution compared to your existing infrastructure.
Step-by-Step: Using the TCO Calculator for ROAI
To perform a comprehensive ROAI analysis, follow these steps to capture the full scope of ownership:
1. Define Your Current Workload
Enter details about your existing infrastructure that the AI solution might replace or augment.
2. Adjust Assumptions
Tailor the calculator to your business environment by adjusting key metrics.
3. Review the TCO Report
The calculator generates a detailed report comparing your current costs against Azure.
Reference:
https://www.synextra.co.uk/knowledge-base/azure-tco-vs-pricing-calculator
https://www.dev4side.com/en/blog/azure-ai
NEW QUESTION # 39
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
Answer: A
Explanation:
" Computer use is a tool in Copilot Studio that lets your agent interact with and automate tasks on a Windows computer. It works with websites and desktop apps by selecting buttons, choosing menus, and entering text into fields on the screen. Describe in natural language what you want computer use to do, and it performs the task on a computer you set up by using a virtual mouse and keyboard. By using computer use, agents can complete tasks even when there ' s no API to connect directly to the system. If a person can use an app or website, computer use can too. You can use computer use for tasks like automated data entry, invoice processing, and data extraction. " Reference: https://learn.microsoft.com/en-us/microsoft-copilot-studio
/computer-use
NEW QUESTION # 40
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:
Verified Answer : =
* 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 # 41
A company has two Microsoft Power Platform environments named Dev1 and Prod1. A Microsoft Copilot Studio agent named Agent1 is built into a solution in the Dev1 environment.
You plan to deploy Agent1 to Prod1.
You need to make Agent1 available to the users in Prod1. The solution must minimize administrative effort.
What should you do?
Answer: C
Explanation:
To deploy a Microsoft Copilot Studio agent to a second environment while minimizing administrative work, you should use managed solutions and Power Platform Pipelines (or manual export/import if pipelines are not configured).
Follow these steps to move and share the agent effectively:
1. Prepare the Solution (Source Environment)
Since your agent is already in a solution, ensure all its dependencies are included to avoid
"missing component" errors during import.
2. Export and Import
To minimize manual labor, use a Managed Solution for the target environment. Managed solutions are easier to update or remove later and prevent accidental direct edits in production.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-solutions-import-export
NEW QUESTION # 42
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:
Explanation:
In Microsoft Copilot Studio analytics , each metric is designed to measure a different aspect of agent performance.
For the first requirement, the metric that tracks the percentage of engaged sessions escalated to a live customer service representative is Escalation rate . This directly measures how often conversations are handed off from the agent to a human.
For the second requirement, the metric that helps identify queries causing a knowledge source error is Answer quality . This area evaluates how well the agent responds and includes issues related to grounded answers, failed responses, and knowledge-source-related problems.
Why the other options are not correct:
* Customer Satisfaction (CSAT) score measures user satisfaction, not escalation percentage or knowledge source errors.
* Engagement rate measures whether users actively interact with the agent, not whether sessions are escalated or whether a knowledge source failed.
NEW QUESTION # 43
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