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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build intelligent apps with Copilot and agents | 30% | - Create and manage agents with Copilot Studio
|
| Topic 2: Extend and secure intelligent solutions | 20% | - Secure access and data
|
| Topic 3: Enhance solutions with AI and automation | 22% | - Use AI Hub models and pre-built capabilities
|
| Topic 4: Create a foundation for intelligent applications | 28% | - Implement responsible AI principles
|
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NEW QUESTION # 87
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.
Answer: A,E
Explanation:
A user-defined function and a component library solve different forms of duplication in the canvas-app estate.
A user-defined function centralizes reusable Power Fx logic behind a named, parameterized function, so formulas do not need to be copied and maintained independently. A component library stores reusable canvas components that can be consumed by multiple apps and updated through a controlled component lifecycle.
That directly supports the requirement for reusable UI elements across applications. A collection is an in- memory table used during an app session, not a cross-application logic package. A context variable is screen scoped and cannot standardize behavior across apps. A named formula can centralize a reusable calculation within an app, but the question's paired requirements emphasize reusable logic and reusable UI across multiple applications; the function and component library provide the complete combination. The components should be packaged in solutions and governed through ALM so dependent apps receive tested versions. The Study Guide names "user-defined functions" and "component libraries" within its exact objective to "create reuseable components," making B and D the aligned selections.
Study Guide reference/topic: "Create canvas apps - Create reusable components." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 88
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: C,E
Explanation:
The app should pass the current user-entered values as prompt parameters and invoke the prompt directly from the button's Power Fx logic. Parameters ensure that the generated summary is grounded in the text currently displayed in the app rather than stale or hard-coded data. Direct invocation from the button provides the immediate interactive behavior required by the scenario. A cloud flow can also consume a prompt, but it adds an orchestration layer that is u nnecessary when the requirement is limited to a synchronous canvas-app interaction. Publishing the prompt is part of making a completed component available, but it does not execute the prompt or provide the current input. Updating general app settings is unrelated. The button formula should validate blank content, handle errors, and place the returned summary in a variable or control only after a successful response. The prompt's inputs and output should be typed and tested for expected maximum lengths. The Study Guide explicitly includes "add inputs to a prompt" and "consume a prompt in apps." Options B and C meet those two runtime responsibilities.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume a prompt in apps." AB-410 Study Guide | Microsoft Learn technical reference
Topic 1, 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.
NEW QUESTION # 89
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result these questions will not appear on the review screen.
A company integrates external systems into Power Automate by using cloud flows.
The company must connect to external services with different characteristics and determine whether a custom connector is required.
You need to evaluate if a custom connector is necessary to meet the requirements.
Solution: A cloud flow must call a REST API by using a one-time HTTP action without reuse across flows.
Does the solution meet the goal?
Answer: A
Explanation:
The requirement does not make a custom connector necessary. A one-time, non-reusable REST request can be implemented with an appropriate HTTP action by supplying the endpoint, method, authentication, headers, query parameters, and request body. The response can then be parsed or referenced in that same flow. A custom connector is most valuable when an API must be exposed as reusable, named operations across several flows or apps, when makers need a shared schema and authentication definition, or when governance requires a managed API abstraction. The scenario explicitly states that reuse is not required, so building and maintaining that abstraction would be unnecessary. The designer must still confirm that the HTTP action supports the service's authentication method, that licensing is available, and that environment data policies allow the endpoint. Those implementation constraints do not change the connector decision given. The Study Guide objective is "evaluate and recommend connectors," which requires distinguishing a direct HTTP call from a reusable custom connector. Since the proposed one-off call can be made natively, the answer is No.
Study Guide reference/topic: "Create cloud flows - Evaluate and recommend connectors." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 90
A company wants to reduce the cost of calling a large language model while maintaining acceptable response quality for simple requests. Which strategy should be considered?
Answer: D
Explanation:
Using smaller or specialized models for simpler tasks can reduce inference costs while maintaining acceptable performance. Large models should be reserved for complex reasoning scenarios. Increasing token usage and duplicate requests increase expenses. Effective AI architecture uses workload classification and selects appropriate models based on business requirements.
NEW QUESTION # 91
A company uses a prompt to generate responses based on internal support knowledge.
The company requires the prompt to minimize generation of unsupported and incorrect information while ensuring responses remain useful.
You need to tune the prompt model settings.
How should you configure the model settings?
Answer: A
Explanation:
To minimize unsupported information and ensure factual accuracy, you should decrease the temperature setting to a low value, typically between 0.0 and 0.3. Lowering the temperature forces the model to prioritize the most probable words, making the responses conservative, deterministic, and tightly aligned with your internal support knowledge base.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/optimize-prompts-topic-configuration
NEW QUESTION # 92
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