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| Section | Weight | Objectives |
|---|---|---|
| Create a foundation for intelligent applications | 25-30% | - Design Microsoft Power Platform solutions using AI-enabled tools
|
| Build business application logic and automation | 40-45% | - Create cloud flows
|
| Create intelligent applications | 25-30% | - Create canvas apps
|
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NEW QUESTION # 88
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 # 89
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Field technicians use an outdated mobile app to log maintenance activities and capture asset readings in SharePoint Online.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The outdated mobile app must be replaced with a new app that supports responsive design principles, provides full control over the UI, and allows technicians to take and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 configure the generative AI reusability requirements. What should you do?
Answer: C
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.
To achieve the requirement of managing customer complaint responses and creating custom inspection reports that align with a specific template, you should Call the prompt from a Power Automate cloud flow.
Automated Document Generation: Generating an inspection report matching an existing template requires background document-generation engines, file handling, and saving capabilities (e.g., population into SharePoint, Word Online, or OneDrive). Power Automate is specifically engineered to orchestrate these multi-step background enterprise automations.
Multi-Step Workflow Orchestration: A single automated cloud flow can naturally string together multiple tasks sequentially. For instance, it can ingest incoming data, run the AI Builder Run a prompt action to draft the empathetic complaint response, use a second prompt action to structure the template report, and then distribute both as needed.
Human-in-the-Loop Capabilities: Microsoft highly recommends adding human oversight for generative AI outputs in complaint responses before sending them to customers. Cloud flows make it easy to seamlessly pause mid-process and use Power Automate Approvals to let a human review the content.
Reference:
https://learn.microsoft.com/en-us/ai-builder/use-a-custom-prompt-in-app
NEW QUESTION # 90
A company maintains an AI-enabled Power Platform solution that includes prompts, cloud flows, and Dataverse components. The solution is deployed to a production environment.
The company requires the following updates to the solution:
# New features must be released regularly.
# Updates must be validated before release.
# Deployed updates must be able to be removed from production if necessary.
You need to update the solution while maintaining release control and following Microsoft best practices.
What should you do?
Answer: C
Explanation:
The solution should be updated in the development environment and deployed to production as a managed solution. Development is where makers modify unmanaged components, run validation, and prepare a versioned release. The managed package is then imported through the organization's controlled deployment process. Managed solutions preserve publisher ownership, support upgrades and patches, and can be removed from production when dependencies and data considerations permit. Running Solution Checker is an important validation step, but option B does not include the required deployment mechanism and therefore is not a complete answer. Importing an unmanaged solution into production merges customizations into the active unmanaged layer and makes controlled removal difficult. Editing a customizations file manually bypasses supported maker and ALM processes and introduces avoidable risk. In practice, the release should also be tested in a separate validation environment before production. The AB-410 Study Guide expressly includes "apply a Microsoft Power Platform solution and ALM strategy." Option A is the only choice that combines an appropriate source environment with the correct production package type and release-control model.
Study Guide reference/topic: "Design Power Platform solutions - Apply a solution and ALM strategy." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 91
A company deploys a model-driven app to multiple departments.
The company requires the following app functionality:
# Enable users to open the app.
# Determine why users cannot access the app.
You need to configure app access.
Which actions should you perform? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Enable users to open the app - Share the app and assign appropriate security roles; Diagnose access - Run the App access checker
To enable users to open the model-driven app, the maker must share the app with the appropriate security roles and ensure users are assigned those roles. App visibility alone is not sufficient if the role lacks privileges to required Dataverse tables, but role-based sharing is the central app-access control. When a particular user still cannot open the app, the App access checker is the targeted diagnostic. It evaluates the user's app sharing, security roles, licensing, and related configuration and reports the access path or missing requirement. The solution checker reviews solution quality and component issues; it does not diagnose a user's effective app entitlement. Reviewing table permissions manually can be useful after a specific role problem is identified, but it is not the broad first diagnostic represented in the answer area. The checker should be run against the affected user identity and the exact app. This maps directly to the Study Guide objectives "configure access to model-driven apps" and "configure security." The two actions establish access and then provide a supported way to determine why effective access differs from the intended configuration.
Study Guide reference/topic: "Create model-driven apps - Configure access to model-driven apps." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 92
A company uses Dataverse to configure derived values for sales records.
The company requires the following functionality:
# Evaluate an expression by using numerical columns in the same row.
# Calculate the result dynamically when accessed.
# Ensure the result is NOT stored as part of the table data.
The solution must minimize use of custom code.
You need to configure the app.
What should you do?
Answer: B
Explanation:
A formula column is designed for a dynamically derived value based on columns in the same Dataverse row.
The maker defines a Power Fx expression using the numerical source columns, and Dataverse exposes the result as a read-only calculated value when it is requested. The formula definition is maintained rather than a manually populated result column, so the application does not need a background job to keep the value synchronized. A real-time workflow or business rule would write a value into stored data and would have to run again whenever an input changes. A plug-in can calculate a value during a transaction or retrieval, but it introduces custom server-side code and deployment overhead that the requirement specifically asks to minimize. The formula must use supported data types and functions, and makers should consider precision, blank values, and division-by-zero behavior. Microsoft's formula-column documentation describes these columns as displaying calculated Dataverse values through Power Fx. The Study Guide includes "configure calculated, rollup, and formula columns." Option A is therefore the native low-code choice for the same-row dynamic calculation.
Study Guide reference/topic: "Implement business and process logic - Configure formula columns." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 93
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