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| Section | Weight | Objectives |
|---|---|---|
| Enhance solutions with AI and automation | 22% | - Build automation with Power Automate
|
| Build intelligent apps with Copilot and agents | 30% | - Create and manage agents with Copilot Studio
|
| Extend and secure intelligent solutions | 20% | - Deploy, monitor, and maintain solutions
|
| Create a foundation for intelligent applications | 28% | - Implement responsible AI principles
|
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NEW QUESTION # 23
A company is designing a Power Platform solution that includes a model-driven app used by customer service representatives to manage support cases.
The company is evaluating which solution component to include in the app .
The solution must meet the following requirements:
# Users must interact with system-generated responses while working within forms and views.
# Responses must reflect the current data and context in the app.
# Users must remain within the app while interacting with the solution.
You need to recommend solution components for the app.
Which two components should you recommend? Each correct answer presents a complete solution. NOTE:
Each correct selection is worth one point.
Answer: B,C
Explanation:
A Copilot Studio agent and an app assistant agent are the two components designed for contextual, in-app conversational interaction. A Copilot Studio agent can be embedded or made available within the business solution and can use configured knowledge, actions, and conversation topics to answer users while they remain in the application. An app assistant agent is intended to assist users in the context of the model-driven app and its current business data. Business rules execute deterministic form or table logic; they cannot provide a conversational response experience. Smart assist surfaces suggested knowledge articles or similar recommendations in supported customer-service scenarios, but it is not the general conversational component described by the requirements. The implementation must also govern grounding sources and security so an agent cannot reveal records the user is not authorized to access. The AB-410 Study Guide asks candidates to
"evaluate built-in agents to include in the business solution" and to integrate Copilot capabilities into model- driven experiences. Those objectives support selecting the two agent-based components rather than validation or recommendation-only features.
Study Guide reference/topic: "Design Power Platform solutions - Evaluate built-in agents." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 24
A company maintains a Power Platform solution that includes prompts, cloud flows, and Dataverse components. Multiple development teams are responsible for different features within the solution.
The development teams have the following requirements:
- Teams must develop and update the same features independently.
- Updates must not overwrite changes made by other teams.
- Production deployments must remain controlled and predictable.
You need to recommend an ALM approach that supports independent development and controlled deployment in line with Microsoft best practices.
What should you recommend?
Answer: B
Explanation:
Isolation prevents overwrites: Giving each team a separate development environment ensures they can build, edit, and test their features independently. It completely eliminates the risk of one team accidentally overwriting or breaking another team's components (like shared Cloud Flows or Dataverse tables) during active development.
Controlled production deployment: Deploying components as managed solutions into the production environment is a strict Microsoft ALM best practice. Managed solutions protect the target environment from unauthorized direct edits and allow for clean, predictable updates and rollbacks.
True parallel development: Segmenting features into individual solutions across isolated environments enables distinct release cadences for each development team.
References:
https://learn.microsoft.com/en-us/power-platform/alm/organize-solutions
NEW QUESTION # 25
A company uses a prompt to generate responses based on internal support knowledge.
The company requires the prompt to minimize generation of unsupported and incorrect information while ensuring responses remain useful.
You need to tune the prompt model settings.
How should you configure the model settings?
Answer: D
Explanation:
To minimize unsupported information and ensure factual accuracy, you should decrease the temperature setting to a low value, typically between 0.0 and 0.3. Lowering the temperature forces the model to prioritize the most probable words, making the responses conservative, deterministic, and tightly aligned with your internal support knowledge base.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/optimize-prompts-topic-configuration
NEW QUESTION # 26
A company is developing an intelligent customer support application using Azure AI services. The solution must analyze customer conversations, identify user intent, and provide automated responses while maintaining conversation context across multiple interactions. The development team wants a managed service requiring minimal machine learning expertise. Which Azure capability should be implemented?
Answer: D
Explanation:
Azure AI Language conversational language understanding provides a managed natural language processing capability for identifying intents, entities, and user utterances. It is designed for conversational applications without requiring teams to build and train complex machine learning models. Azure Machine Learning and AKS are more suitable for custom ML scenarios requiring greater control over model development and deployment.
NEW QUESTION # 27
A company builds a canvas app that processes images submitted by users and must classify them using an existing AI Builder model.
The app must generate predictions during user interaction.
You need to consume the AI model natively.
What should you do?
Answer: A
Explanation:
To process user-submitted images natively within a canvas app using an existing AI Builder model, you should Call the model from app logic.
By adding the AI model as a data source and calling its .Predict() method in your Power Fx formulas, the app evaluates the images dynamically while the user interacts with it.
Reference:
https://learn.microsoft.com/en-us/ai-builder/powerfx-in-powerapps
NEW QUESTION # 28
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