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| Section | Objectives |
|---|---|
| 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
|
| Implement automation and orchestration | - Process automation
|
| Build intelligent applications on Microsoft Power Platform | - Application development
|
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NEW QUESTION # 52
You are building a model-driven app that uses a Microsoft Dataverse table named Accounts.
Sales representatives use the app to manage customer records.
Sales representatives report that too many records are displayed, including inactive and unrelated accounts where the sales representative isn't the record owner.
You need to ensure only relevant records are displayed for the users.
What are two ways to complete the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
[E]
Filters restrict rows: View filters apply logical criteria (like Status Equals Active and Owner Equals Current User) to limit which specific records appear in the list.
[C]
The best alternative approach to view filters is to Apply security roles to restrict view access.
By adjusting the read privileges within your Microsoft Dataverse Security Roles, you can globally limit a sales representative's record visibility to only the accounts, contacts, or opportunities they own or that fall under their business unit, automatically filtering out unrelated records.
Incorrect:
[Not A]
View columns only control what information is visible (such as the account name or phone number) for the records that are already returned.
Reference:
https://learn.microsoft.com/en-us/power-apps/user/grid-filters-advanced
NEW QUESTION # 53
An insurance company is implementing a customer service solution in Microsoft Dataverse to track interactions and manage issue resolution. The data model includes tables for customer information, support cases, and agents .
The data model includes the following requirements:
# Link each support case to a customer.
# Provide information on the children of customers.
# Record history of agents assigned to customers.
You need to configure table relationships.
Which relationships should you use? To answer, move the appropriate relationship to the correct requirement.
You may use each relationship once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Support case to customer - One-to-many; Customer children - One-to-many; Agent assignment history - Connections
Each support case should reference one customer through a many-to-one lookup, which is configured from the customer perspective as a one-to-many relationship: one customer can have many cases. Customer children also require a self-referencing one-to-many relationship, allowing one customer row to be the parent of multiple child customer rows. Historical agent assignments are different. A conventional lookup retains only the current related row unless a separate history table is introduced. Dataverse Connections provide a flexible, role-based association between records and can retain multiple agent-to-customer links over time without redesigning the standard tables. A one-to-one relationship would prevent a customer from having multiple cases or children, and a many-to-many relationship is unnecessary where each case or child has one parent. The designer should define relationship roles and views so users can interpret the connection history consistently. The AB-410 Study Guide includes the exact objective "configure table relationships." The correct mapping uses one-to-many relationships for structured parent/child data and Connections for the flexible assignment-history requirement.
Study Guide reference/topic: "Build data models - Configure table relationships." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 54
A development team builds an Azure OpenAI application that generates business reports. The application occasionally produces incorrect facts even though the prompts are clear. The team wants to reduce inaccurate responses without retraining the model. Which approach should be used?
Answer: D
Explanation:
Grounding improves generative AI accuracy by providing trusted external information during generation. Using retrieval techniques allows the model to reference authoritative business data instead of relying only on pretrained knowledge. Increasing compute resources does not improve factual accuracy. Prompt formatting helps output consistency but does not solve knowledge limitations.
NEW QUESTION # 55
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: D
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 # 56
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: C
NEW QUESTION # 57
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