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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Create a foundation for intelligent applications | 28% | - Design solutions using AI-enabled tools
|
| Topic 2: Enhance solutions with AI and automation | 22% | - Use AI Hub models and pre-built capabilities
|
| Topic 3: Extend and secure intelligent solutions | 20% | - Deploy, monitor, and maintain solutions
|
| Topic 4: Build intelligent apps with Copilot and agents | 30% | - Integrate Copilot into canvas and model-driven apps
|
>> AB-410 Zertifizierungsfragen <<
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59. Frage
A company uses Microsoft Dataverse to store customer records on the Account table. A business process flow is configured for the table.
The company requires a Power Automate cloud flow that validates and corrects the format of the customer account number. The Power Automate flow must run whenever data in this field changes.
You need to recommend a trigger for the cloud flow.
Which trigger should you recommend?
Antwort: A
Begründung:
The cloud flow trigger should fire when a row is modified, specifically targeted to the Account table, with the "Select columns" (column filter) parameter set to the logical name of the customer account number column.
Correct Trigger Configuration
Connector: Microsoft Dataverse
Trigger: When a row is added, modified or deleted
Change type: Modified (or Added or Modified if you also need to check the format upon record creation) Table name: Accounts Reference:
https://learn.microsoft.com/en-us/power-automate/dataverse/overview
60. Frage
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?
Antwort: D
Begründung:
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.
61. Frage
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?
Antwort: A
Begründung:
Building a Custom Connector is an ideal and highly appropriate solution for this scenario. It acts as a wrapper around the third-party AI's REST API, allowing you to easily handle custom requests, format payloads, and manage specific authentication methods (e.g., API Keys, OAuth
2.0) within the Power Platform.
Reference:
https://learn.microsoft.com/en-us/connectors/custom-connectors
62. Frage
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 resolve the issues with the canvas apps.
Which two configurations should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Antwort: B,E
Begründung:
Scenario: Duplicate logic exists across multiple canvas apps.
The two features that can be used to remedy the problem of duplicate logic existing across multiple canvas apps are component library and User defined function.
Component library: This feature allows you to build reusable canvas components (containing both visual elements and custom properties with calculation or behavioral logic) and host them centrally. Multiple canvas apps can then import these components from the library, providing a single source of truth that simplifies maintenance across your entire app ecosystem.
User defined function (UDF): This Power Fx feature enables you to write a custom formula once, complete with input parameters and an output data type. By packaging this pure behavioral or calculation logic into an enhanced component property within a component library, the custom function can be called globally across multiple apps Reference:
https://learn.microsoft.com/en-us/power-apps/maker/canvas-apps/working-with-large-apps
63. Frage
A company uses a Dataverse table to manage support cases.
You must provide employees with a summary of case information derived from multiple columns without requiring a manual review of the data.
The solution has the following requirements:
# Generate a case summary following the given instructions.
# Include the context of the current record in the generated output.
You need to configure the solution.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Antwort: B,C
Begründung:
The maker should define a prompt that references the support-case fields and create the destination column with the Prompt data type. The prompt contains the instructions for producing the case summary and declares the record inputs needed to ground that output. The prompt column then makes the generated summary available in the Dataverse/model-driven app context for the current row. A formula column performs deterministic Power Fx computation and cannot generate a natural-language summary from multiple fields. A business rule can set or validate values, but it does not invoke a generative model. The prompt should limit inputs to the approved columns, specify the required structure, and handle empty or contradictory case data so the generated content remains useful. Security on the source fields must still be respected, because an AI- generated summary must not become a route around restricted data. The AB-410 Study Guide lists "configure prompt columns" under Build data models and "add inputs to a prompt" under AI Hub. Options A and D supply the instruction definition and the Dataverse column behavior required for record-context generation.
Study Guide reference/topic: "Build data models - Configure prompt columns." AB-410 Study Guide | Microsoft Learn technical reference
64. Frage
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