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| Section | Objectives |
|---|---|
| Topic 1: Design intelligent applications with Copilot and natural language | - AI-first application design
|
| Topic 2: Build intelligent applications on Microsoft Power Platform | - Application development
|
| Topic 3: Governance and application lifecycle management | - Responsible AI and ALM
|
| Topic 4: Embed agents and AI experiences | - Agent integration
|
| Topic 5: Implement automation and orchestration | - Process automation
|
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78. Frage
A company builds a canvas app to display and manipulate large datasets across multiple screens.
Users report the app takes a long time to load data when opened.
You need to optimize performance when the app is started.
What are three actions you can perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Antwort: A,D,E
Begründung:
To optimize the startup performance of a Canvas app handling large datasets, you must focus on reducing the initial data payload and parallelizing operations.Here are three high-impact steps you can take:
[B] Remove data retrieval calls from App.OnStart:
Heavy data loading in App.OnStart blocks the app from rendering, forcing users to stare at a loading screen. Moving these calls to the OnVisible property of the first screen allows the UI to load immediately while data fetches in the background.
[D] Enable Concurrent Data Loading
By default, Power Apps loads data sources sequentially. You can use the Concurrent function in the App.OnStart or App.StartScreen property to load multiple collections or data sources at the exact same time. This dramatically reduces initial wait times.
[E] Use the ClearCollect() function to cache data: Storing large, frequently accessed datasets locally in a collection prevents the app from making repeated, slow network calls to the data source across multiple screens.
Reference:
https://learn.microsoft.com/en-us/power-apps/guidance/coding-guidelines/code-optimization
79. Frage
A company plans to implement an automated process to retrieve and analyze customer feedback.
The company requires a solution that:
# Autonomously analyzes feedback.
# Stores results for reporting.
# Triggers an escalation for negative sentiment.
You need to design the solution.
Which three components should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. (Choose three.)
Antwort: A,C,D
Begründung:
The solution requires an AI Builder prompt, a cloud flow, and a Dataverse table. The prompt performs the autonomous semantic analysis of each feedback item, including sentiment or other instructed classifications.
The cloud flow supplies the event-driven orchestration: it retrieves new feedback, calls the prompt, interprets the result, writes the output, and branches to an escalation action when sentiment is negative. Dataverse provides persistent, queryable storage for the original feedback, model result, status, and reporting fields. A canvas app may be useful for reviewing results but is not required for autonomous execution. A business rule can validate or adjust Dataverse data but cannot independently call the generative model and orchestrate external escalation. The flow should include explicit failure handling, confidence or result validation, and an idempotent design so retries do not create duplicate escalations. Approved data policies must also permit the connectors used by the flow. The Study Guide covers "consume a prompt in cloud flows," "configure cloud flow actions," and the creation of Dataverse-backed business solutions. A, B, and E form the complete analysis, automation, and persistence architecture.
Study Guide reference/topic: "Create cloud flows; Create prompts and models in AI Hub." AB-410 Study Guide | Microsoft Learn technical reference
80. Frage
A company has a product report in PDF format. The report must be rewritten with formal vocabulary and cadence.
The company requires a prompt that can be used within a canvas app to rewrite the report as a Word document. The prompt must reference a business glossary stored in a SharePoint site.
You need to make a suggestion for the expectations portion of the prompt to generate a report summary correctly.
What should you suggest?
Antwort: B
Begründung:
The best approach is "The summary should highlight key themes, product features, and recurring feedback." Choosing this approach is best because it provides explicit, actionable instructions to the AI model.
Incorrect:
[Not B]
Simply telling the AI to "use the PDF and the glossary" only identifies the sources of data; it does not explain what to do with that data or how to structure the output. Combining specific criteria (themes, features, feedback) with your source references ensures a high-quality, targeted summary.
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/prompt-column
81. 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:
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/
82. Frage
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?
Antwort: C
Begründung:
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
83. Frage
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