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| Section | Objectives |
|---|---|
| Embed agents and AI experiences | - Agent integration
|
| Design intelligent applications with Copilot and natural language | - AI-first application design
|
| Build intelligent applications on Microsoft Power Platform | - Application development
|
| Implement automation and orchestration | - Process automation
|
| Governance and application lifecycle management | - Responsible AI and ALM
|
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NEW QUESTION # 19
You need to run the App access-checker.
What should you use as the input?
Answer: D
Explanation:
The App access checker requires the individual user as its input, so the new QA tester's email address is the appropriate value. The checker evaluates why that user can or cannot open a model-driven app by examining app sharing, assigned security roles, licenses, and relevant environment access. A Microsoft Entra security- group name or a Dataverse group-team name is not the subject of the diagnostic; those objects can influence access, but the tool performs its analysis for a specific user identity. The manager's username is unrelated to the tester's failure. In this case, group-team membership may be synchronized or evaluated indirectly, and the checker can reveal the effective access result even when the user is not visibly listed as a conventional team member. Administrators should also verify that the environment security group, license assignment, app sharing, and table privileges are aligned. Its content maps to the Study Guide objective "configure access to model-driven apps," including use of the App access checker for targeted diagnosis.
Study Guide reference/topic: "Create model-driven apps - Configure access to model-driven apps." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 20
A company develops an AI chatbot using Azure OpenAI. Security administrators require that customer data must not be used to train public foundation models. Which statement correctly describes Azure OpenAI data handling?
Answer: A
Explanation:
Azure OpenAI provides enterprise data protection where customer prompts, completions, and training data are not used to train public OpenAI models. Data isolation and security controls are key enterprise features. Organizations can build applications while maintaining control over their information. Public model improvement processes are separate from Azure OpenAI customer workloads.
NEW QUESTION # 21
An accounting team is assigned to a model-driven app to manage financial data.
The app is currently visible in the app launcher but inaccessible to the accounting team.
Users must be able to:
# Open the app.
# Access and record financial data.
You need to resolve the access issues.
What should you do?
Answer: D
Explanation:
Assigning the appropriate security roles is the complete action because model-driven app access and Dataverse data access are both role driven. The app must be shared with the role, and the role must grant the accounting team the table privileges required to read and create or update the financial records. Configuring app visibility privileges alone may make the app tile appear, but it does not establish the underlying table rights. Setting an app as default affects navigation or launch preference and does not grant access. Granting table-level permissions is only one part of the required configuration and, in Dataverse, those privileges are normally delivered through a security role; option C represents the combined mechanism expected by the question. The administrator should use least privilege, choose the correct access depth, and include privileges for dependent tables and processes used by the app. Existing visibility in the launcher is not proof of effective authorization. The AB-410 Study Guide includes both "configure access to model-driven apps" and
"configure security." Appropriate role assignment aligns the user, the shared app, and the table operations in one enforceable access model.
Study Guide reference/topic: "Create model-driven apps - Configure access to model-driven apps." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 22
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 provide a solution for field technicians to create custom inspection reports natively within the new app. What should you do?
Answer: A
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.
You should use a custom prompt.
Generative AI Capability: Power Platform AI Builder prompts leverage large language models (like GPT) to natively handle generative text workflows.
Complaint Handling: A custom prompt can be built specifically to analyze customer feedback and automatically generate draft email or text responses.
Template Alignment: You can embed your exact inspection report layout and instructions directly inside the custom prompt's logic. This ensures that the generated outputs perfectly match the structure of your existing template.
Reference:
https://learn.microsoft.com/en-us/ai-builder/overview
NEW QUESTION # 23
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: D
Explanation:
The best option to use is a calculated column (or its modern equivalent, a formula column).
Why?
Evaluates expressions in the same row: Calculated and formula columns natively execute calculations using other numerical columns belonging to the exact same row.
Calculates dynamically when accessed: Calculations are performed on-the-fly and evaluated in real-time when the data is read or displayed.
Not stored in table data: Because the value is dynamically generated at the database or query level whenever requested, it does not permanently occupy physical table data storage.
Minimizes custom code: These are out-of-the-box, low-code configurations inside Microsoft Dataverse utilizing simple Excel-like or Power Fx formulas.
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/types-of-fields
NEW QUESTION # 24
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