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| Section | Weight | Objectives |
|---|---|---|
| Build intelligent apps with Copilot and agents | 30% | - Integrate Copilot into canvas and model-driven apps
|
| Extend and secure intelligent solutions | 20% | - Deploy, monitor, and maintain solutions
|
| Create a foundation for intelligent applications | 28% | - Implement responsible AI principles
|
| Enhance solutions with AI and automation | 22% | - Build automation with Power Automate
|
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NEW QUESTION # 72
A company is using Microsoft Dataverse.
You must integrate Dataverse with a third-party AI service. The solution requires authentication and custom request handling to send and receive data.
You need to enable the integration.
What should you do?
Answer: A
Explanation:
Use a Custom Connector. It is built specifically to wrap REST APIs and natively supports the required custom request handling and authentication needed to send and receive data with third- party AI services.
Reference:
https://learn.microsoft.com/en-us/connectors/commondataserviceforapps/
NEW QUESTION # 73
Case Study 2 - 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Field technicians use an outdated mobile app to log maintenance activities and capture asset readings in SharePoint Online.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.
You need to implement a generative AI solution that meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Scenario: The company requires a generative AI solution to manage customer complaint responses and create custom inspection reports that align to an existing template You should use a custom prompt (starting from a blank prompt) for the template alignment, and a pre-built model for the complaint responses.
Task 1: Inspection Reports (Aligning to an Existing Template)
Approach: Start from a blank prompt (Custom Prompt).
Reason: Pre-built templates are rigid and designed for standard tasks like summarizing text or translating language. To make an AI output match your specific corporate inspection layout, tone, and data points, you need to write custom system instructions and ground it with your specific template rules.
Task 2: Customer Complaint Responses
Approach: Use a pre-built model (specifically the "Reply to a complaint" or "Create text with GPT" pre-built models in AI Builder).
Reason: Microsoft provides pre-trained, ready-to-use models optimized exactly for sentiment analysis and standard customer service drafting. This saves you from writing complex prompts from scratch for standard business scenarios.
Reference:
https://learn.microsoft.com/en-us/dynamics365/field-service/faqs-inspection-designer
NEW QUESTION # 74
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: A,C
Explanation:
To fulfill your requirements, you should take the following two steps: Invoke the prompt directly from app logic and Pass user input as parameters to the prompt.
Invoke the prompt directly from app logic: Power Apps canvas apps allow you to call Dataverse custom prompts directly using the AIClassify, AISentiment, or AISummarize functions (or your specific custom prompt action) right from a button's OnSelect property. This fulfills the requirement to generate the summary immediately without the added latency or overhead of triggering a Power Automate cloud flow.
Pass user input as parameters to the prompt: To satisfy the requirement of using current user- entered data, you must map the text from your app's input fields (e.g., TextInput1.Text) directly into the input parameters defined within your custom prompt.
Reference:
https://learn.microsoft.com/en-us/ai-builder/use-a-custom-prompt-in-app
NEW QUESTION # 75
A company is using a model-driven app to process orders. The company uses a custom integration to add orders to Microsoft Dataverse from two separate point-of-sale solutions.
Order numbers are stored in a column on an Order table named Number. The order numbers are prefixed with the value POS1- or POS2- depending on which point-of-sale system they originated from.
The company creates a calculated text column to store the clean order number. The clean order number must remove the point-of-sale system prefix.
You need to configure the action on the calculated column.
Which formula should you use?
Answer: E
Explanation:
To remove the point-of-sale system prefix, you should use the TRIMLEFT formula.
The exact configuration requires passing the Number column as the first parameter and the number of characters to remove (5) as the second parameter: TRIMLEFT(Number, 5).
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/define-calculated-fields
NEW QUESTION # 76
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: D
Explanation:
The existing AI Builder classification model should be invoked from the canvas app through Power Fx. The maker adds the model as an app data source or AI capability and calls its prediction operation from the relevant user action, passing the current image. The returned classification and confidence can then be stored in variables, displayed to the user, or written to Dataverse. A static formula cannot reproduce a trained image model's inference. Refining the model with additional images is a training-lifecycle task and may improve quality later, but it does not consume the existing model during the current interaction. Exporting the containing solution transports components between environments; it does not execute a prediction. The app formula should validate that an image is present, handle service errors, and define what happens when confidence is below the accepted threshold. The Study Guide contains the exact objective "consume an AI model in apps." Option A is the native runtime integration that classifies each user-submitted image during the active canvas-app session.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume an AI model in apps." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 77
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