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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Create a foundation for intelligent applications | 28% | - Design solutions using AI-enabled tools
|
| Topic 2: Extend and secure intelligent solutions | 20% | - Deploy, monitor, and maintain solutions
|
| Topic 3: Enhance solutions with AI and automation | 22% | - Build automation with Power Automate
|
| Topic 4: Build intelligent apps with Copilot and agents | 30% | - Integrate Copilot into canvas and model-driven apps
|
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NEW QUESTION # 52
A developer creates an intelligent document processing solution that extracts invoice numbers, vendor information, and payment totals from scanned PDF files. The documents have different layouts depending on the supplier. The solution should minimize manual model training while supporting structured extraction. Which service should be selected?
Answer: C
Explanation:
Azure AI Document Intelligence is specifically designed for extracting structured information from documents with varying layouts. It provides prebuilt and custom models for invoices, receipts, and forms. OCR only extracts text without understanding document structure. Azure AI Search and embeddings improve retrieval scenarios but do not directly perform document field extraction.
NEW QUESTION # 53
A developer wants a generative AI application to consistently return responses in a structured format that another application can process automatically. Which technique should be applied?
Answer: B
Explanation:
Prompt engineering can define expected response formats, including JSON structures, fields, and formatting requirements. This improves interoperability between AI-generated responses and downstream applications. Increasing randomness reduces consistency, while removing instructions weakens model behavior control. Validation logic should complement AI output processing rather than being disabled.
NEW QUESTION # 54
A company builds a canvas app for support agents to enter customer notes and generate a summarized response by using a custom prompt.
The company requires the following functionality built into the prompt:
# Generate the summary immediately when the agent selects a button in the app.
# Supply current user-entered data to the prompt.
You need to execute the prompt.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
The app should pass the current user-entered values as prompt parameters and invoke the prompt directly from the button's Power Fx logic. Parameters ensure that the generated summary is grounded in the text currently displayed in the app rather than stale or hard-coded data. Direct invocation from the button provides the immediate interactive behavior required by the scenario. A cloud flow can also consume a prompt, but it adds an orchestration layer that is u nnecessary when the requirement is limited to a synchronous canvas-app interaction. Publishing the prompt is part of making a completed component available, but it does not execute the prompt or provide the current input. Updating general app settings is unrelated. The button formula should validate blank content, handle errors, and place the returned summary in a variable or control only after a successful response. The prompt's inputs and output should be typed and tested for expected maximum lengths. The Study Guide explicitly includes "add inputs to a prompt" and "consume a prompt in apps." Options B and C meet those two runtime responsibilities.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume a prompt in apps." AB-410 Study Guide | Microsoft Learn technical reference
Topic 1, Fabrikam Inc
Background
Fabrikam Inc. is an energy provider that operates wind and solar farms across multiple countries and regions.
The company uses Microsoft Power Platform to manage asset maintenance, technician workflows, and predictive analytics.
Field technicians use canvas apps on mobile devices to capture inspection and repair data. Operations managers use model-driven apps to monitor asset performance and maintenance history.
The company plans to enhance its platform by introducing AI-driven insights, improving reuse across applications, and establishing a formal application lifecycle management (ALM) strategy.
Current environment
Applications
Field technicians use an outdated mobile app to log maintenance activities and capture asset readings in SharePoint Online.
Operations managers use a model-driven app to review maintenance records and asset performance trends.
Development practices
Developers build and modify applications directly in the production environment.
There is NO structured ALM process.
Solutions are NOT consistently used to package or deploy application components.
Reusable components are inconsistently implemented across apps, leading to duplication.
AI integration
Fabrikam Inc. plans to actively explore generative AI usage as part of the next phase of work.
Business requirement
Application development
The outdated mobile app must be replaced with a new app that supports responsive design principles, provides full control over the UI, and allows technicians to take and upload photos directly from their mobile devices.
The new app for the field technicians requires the name of the technician to be persisted on the home screen only. In addition, work orders must be displayed within a gallery.
All new apps must connect to a scalable, cloud-based relational data store.
Apps must support efficient data entry for technicians in the field.
Reusable UI elements must be implemented to reduce duplication.
AI capabilities
The company requires a generative AI solution to manage customer complaint responses and create custom inspection reports that align to an existing template.
Customer complaint responses must be generated in applications and based on emails being sent to a mailbox.
Custom AI development must be minimized.
Generative AI reusability
Generative AI solutions must accept dynamic inputs to adapt to different scenarios.
Generative AI solutions must be reusable across different Power Platform services.
Extensibility
Custom UI form components must be reusable across applications.
Use of server-side custom code must be minimized where possible.
Technical requirements
Application lifecycle management (ALM)
Separate environments must be used for development, testing, and production.
Solutions must be used to package application components, segmented based on functional areas.
Managed solutions must be deployed to the production environment.
Validation must occur before deployment to production.
Data processing
Aggregated values across related rows must be displayed for reporting.
Calculations must update automatically as new data is entered. Calculation data must be limited to a maximum length of 10.
Issues
Duplicate logic exists across multiple canvas apps.
Developers frequently overwrite changes in production.
Generative AI solutions produce inconsistent outputs due to missing or inconsistent inputs.
Maintenance metrics are not consistently calculated across related rows.
NEW QUESTION # 55
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:
[B]
A Microsoft Copilot Studio agent fulfills these specific requirements by providing embedded, context-aware AI directly inside the user's workflow without disrupting their activities.
In-Form Interactions: Using the Agent response component, agents can be embedded directly into the layout of a model-driven form. This prevents context switching, allowing users to interact with responses alongside their primary workspace.
Real-Time Context: By leveraging Agent APIs, the agent is automatically passed dynamic app data-such as case details, customer information, and recent interactions-triggering specific topics that tailor responses to the exact current record.
Rich Native Formatting: The solution accommodates Markdown text, images, videos, and Adaptive Cards directly in forms. Customer service representatives can therefore review procedures and examine troubleshooting information without ever leaving the app.
[D]
You should include an App assistant agent (also known as a Copilot sidecar or embedded Copilot agent) in your Power Platform solution.
In-App Interaction: An embedded Copilot Studio agent integrates directly into the model-driven app as a sidepane sidecar or page component. This satisfies the requirement that users must remain within the app.
Context-Aware Responses: Modern app assistant agents inherently access the active form context, row data, and selected view items. This fulfills the requirement that responses must reflect current data and context.
Direct Interaction: Representatives can chat with, trigger, and review system-generated outputs directly beside their active workspace, allowing seamless interaction within forms and views.
Reference:
https://learn.microsoft.com/en-us/power-platform/architecture/reference-architectures/contextual-ai-model-driven-app
NEW QUESTION # 56
A developer needs an AI model to classify incoming emails into categories such as billing, technical support, and complaints. The categories are predefined and training examples are available. Which machine learning approach is appropriate?
Answer: A
Explanation:
Classification models assign data into predefined categories based on learned patterns. Email routing scenarios commonly use supervised classification because labeled examples exist.
Clustering is used when categories are unknown, regression predicts numerical values, and reinforcement learning focuses on decision-making through rewards rather than direct category assignment.
NEW QUESTION # 57
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