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| Section | Objectives |
|---|---|
| Implement automation and orchestration | - Process automation
|
| Build intelligent applications on Microsoft Power Platform | - Application development
|
| Design intelligent applications with Copilot and natural language | - AI-first application design
|
| Governance and application lifecycle management | - Responsible AI and ALM
|
| Embed agents and AI experiences | - Agent integration
|
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NEW QUESTION # 88
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Field technicians use an outdated mobile app to log maintenance activities and capture asset readings in SharePoint Online.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The outdated mobile app must be replaced with a new app that supports responsive design principles, provides full control over the UI, and allows technicians to take and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: 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.
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 # 89
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: A
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 # 90
A company is building a canvas app that spans multiple screens.
The app must store selected values across screens and hold tabular data during a session.
You need to manage application state.
Which two components should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
A global variable is appropriate for a selected scalar value that must remain available while the user moves between screens. It is created with Set and can be referenced anywhere in the canvas app for the remainder of the session unless changed. A collection is an in-memory table, so it is the correct component for holding multiple rows and columns of tabular data that controls such as galleries can consume. Context variables are scoped to a screen and would not reliably satisfy the cross-screen requirement. A data source is an external system or table rather than an application-state container; connecting to one does not itself preserve a temporary selection or session table. The maker should avoid duplicating authoritative data in a collection unless offline or transformation behavior requires it, and should clear sensitive session state when appropriate.
For large datasets, delegable source queries are preferable to indiscriminate collection loading. The Study Guide's exact canvas-app objective is "manage variables and collections." Options A and D match the required tabular and application-wide scalar state types.
Study Guide reference/topic: "Create canvas apps - Manage variables and collections." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 91
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: B
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 # 92
A company is building prompts in Microsoft Copilot Studio to support multiple customer service scenarios.
The company requires prompts to:
# Follow a consistent structure across use cases.
# Be reusable across multiple topics.
# Allow customization for different types of customer requests.
You need to build the prompts.
What should you do?
Answer: B
Explanation:
The maker should select an appropriate template from the prompt library and customize it for each scenario.
Templates provide a consistent starting structure and established prompt pattern while still allowing the instructions, inputs, grounding, and expected output to be adapted to a particular customer request. This balances standardization with scenario-level variation. Model settings influence behavior such as creativity or moderation, but they do not create a reusable prompt structure. Storing prompt text in ordinary topic variables duplicates unmanaged text and does not provide a governed prompt component with its own inputs and lifecycle. Creating unrelated prompts directly inside every topic would repeat structure and make maintenance inconsistent. Customized prompt components should be named clearly, added to solutions, tested against representative inputs, and versioned through ALM. Shared instructions can be kept consistent while scenario- specific requirements remain explicit. The AB-410 Study Guide contains the exact objective "build prompts from templates or from blank." Option D uses the template path for consistency and then applies the customization required for multiple service scenarios.
Study Guide reference/topic: "Create prompts and models in AI Hub - Build prompts from templates." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 93
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