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A second format is a AB-100 web-based practice exam that can take for self-assessment. However, it differs from desktop-based AB-100 practice exam software as it can be taken via any browser, including Chrome, Firefox, Safari, and Opera. This Microsoft AB-100 web-based practice exam does not require any other plugins. You can take this AB-100 self-assessment test on Windows, iOS, Linux, Mac, and Android. It also includes all of the functionalities of desktop AB-100 software and will assist you in passing the AB-100 certification test.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan AI-powered business solutions | 25–30% | - Design overall AI strategy
|
| Topic 2: Design AI-powered business solutions | 25–30% | - Design AI and agents
|
| Topic 3: Deploy AI-powered business solutions | 40–45% | - Manage testing and validation
|
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NEW QUESTION # 68
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:
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
NEW QUESTION # 69
A company deploys a Microsoft Copilot Studio agent that integrates with a Microsoft Power Automate desktop flow.
You need to recommend a testing solution that meets the following requirements:
Test cases must validate the most recent changes to the agent before the agent is released.
The flow must be validated as part of the agent ' s orchestration.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Validate most recent changes # Run tests against the latest unpublished version of the agent; Validate the flow as part of orchestration # Add the flow to the agent as a tool Why the first selection is correct The requirement says the company must validate the most recent changes to the agent before the agent is released . That means testing must happen on the newest working version that has not yet been published to production.
So the correct recommendation is:
Run tests against the latest unpublished version of the agent
This is the safest and most appropriate pre-release testing pattern because it allows the team to:
* verify recent changes before users see them
* catch regressions early
* validate orchestration logic in a controlled state
* reduce production risk
From an AI business solutions perspective, pre-release testing is critical for Copilot agents because even small changes in prompts, tools, orchestration, or data connections can affect:
* response quality
* workflow behavior
* escalation paths
* compliance behavior
* user trust
Testing unpublished changes ensures that governance and quality assurance happen before release, not after deployment.
Why the second selection is correct
The question also requires that the Power Automate desktop flow be validated as part of the agent ' s orchestration .
That means the flow must participate directly in the agent's runtime action path. The correct way to do that is:
Add the flow to the agent as a tool
When a flow is added as a tool, the agent can invoke it during its orchestration. This allows test cases to validate not only the conversational layer, but also the actual execution of the flow inside the end-to-end agent behavior.
From an agentic AI design perspective, tools are how agents connect reasoning with action. If the flow is meant to be part of orchestration, it must be exposed to the agent in a way that allows the agent to call it during task execution.
That is the correct enterprise pattern for validating integrated behavior.
NEW QUESTION # 70
Hotspot Question
A company has a Microsoft Copilot Studio agent for customer support.
You are reviewing and validating the following prompts:
- A prompt that has instructions to "help the customer as best you can"
- A prompt that helps retrieve product information from a knowledge
base
You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 71
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
Answer:
Explanation:
Explanation:
Enforces deployment to only approved Azure regions # Azure Policy; Provides continuous compliance verification # Microsoft Defender for Cloud Why Azure Policy is correct The requirement is to enforce that Azure OpenAI resources can be deployed only in approved Azure regions.
That is exactly what Azure Policy is designed to do. Azure Policy allows organizations to create and assign rules that govern resource deployment and configuration. For regional restrictions, you can define a policy that permits deployments only in allowed locations and denies deployments elsewhere.
From an AI business solutions and cloud governance perspective, Azure Policy is the right preventive control because it acts at deployment time. It helps enforce organizational standards before noncompliant resources are created.
Typical policy use cases include:
restricting allowed Azure regions
enforcing approved SKUs
requiring tags
limiting resource types
ensuring security configuration standards
This is especially important for AI deployments where geography may affect:
regulatory compliance
data residency
internal governance
customer contract obligations
Why Microsoft Defender for Cloud is correct
The second requirement is to provide continuous compliance verification of the resources.
That points to Microsoft Defender for Cloud.
Defender for Cloud continuously assesses Azure resources against security and compliance standards. It provides visibility into resource posture, identifies misconfigurations, and tracks compliance status over time.
This makes it well suited for ongoing verification because it supports:
continuous assessment
compliance dashboards
security posture monitoring
recommendations for remediation
regulatory standard mapping
In enterprise AI deployments, this is critical because governance is not only about blocking bad deployments.
It is also about continuously validating that deployed resources remain compliant as environments evolve.
Why the other options are incorrect
Azure Monitor
Azure Monitor is used for telemetry, logging, metrics, and observability. It is not the primary service for enforcing allowed regions or for formal continuous compliance governance.
Microsoft Purview
Microsoft Purview focuses on data governance, data cataloging, classification, and compliance across data estates. It is not the main control for Azure resource deployment region enforcement.
Microsoft Sentinel
Microsoft Sentinel is a SIEM/SOAR platform for security analytics and threat detection. It is not the service used to enforce deployment locations, and it is not the primary tool for continuous Azure resource compliance verification.
Azure Policy for continuous verification
Azure Policy does provide compliance views, but in this question, the stronger mapping for continuous compliance verification is Microsoft Defender for Cloud, which is specifically designed for continuous security posture and compliance assessment across resources.
Expert reasoning
Use this exam pattern:
Prevent or restrict how Azure resources are deployed # Azure Policy
Continuously assess and verify cloud compliance posture # Microsoft Defender for Cloud
NEW QUESTION # 72
A company has a cloud-based Al solution that uses Azure OpenAI models.
You need to design a monitoring solution that meets the following requirements:
* Monitors performance metrics and operational health for the models
* Monitors Al apps and agents for compliance
* Uses Azure-native capabilities
* Minimizes development effort
What should you use for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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:
Monitors AI app and agents for compliance # Microsoft Purview
Monitors performance metrics and operational health # Azure Monitor
For an Azure-based AI solution using Azure OpenAI models, the best Azure-native monitoring design is to separate:
operational and performance monitoring
compliance and governance monitoring
For performance metrics and operational health, the correct choice is Azure Monitor. It is the standard Azure- native service for collecting telemetry, tracking service health, monitoring metrics, analyzing logs, and alerting on runtime issues. This is the best fit for model and application operational monitoring with minimal development effort.
For monitoring AI apps and agents for compliance, the correct choice is Microsoft Purview. Purview is designed for compliance, governance, data protection, and policy-based oversight across data and AI-related assets. It aligns best with the requirement to monitor AI applications and agents from a compliance perspective.
Why the other options are not the best fit:
Azure API Management is for API exposure, management, and security, not primary compliance or operational monitoring.
Azure Policy is used to enforce and assess Azure resource compliance, but it is not the main tool for monitoring AI apps and agents in the broader compliance/governance sense asked here.
Azure Stream Analytics is for streaming data processing, not this monitoring scenario.
Microsoft Defender is focused on security threat detection and posture, not overall AI compliance monitoring.
NEW QUESTION # 73
......
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