AB-731 Musterprüfungsfragen - AB-731Zertifizierung & AB-731Testfagen

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Microsoft AB-731 Prüfungsplan:

Thema	Einzelheiten
Thema 1	Identify the Business Value of Generative AI Solutions: Covers core generative AI concepts, cost drivers, and business challenges, along with techniques like prompt engineering and RAG that enhance AI value through better data quality, security, and machine learning practices.
Thema 2	Identify an Implementation and Adoption Strategy for Microsoft's AI Apps and Services: Covers responsible AI principles, governance, and organizational adoption planning, including AI councils, champion programs, and an understanding of Copilot and Azure AI licensing models.
Thema 3	Identify Benefits, Capabilities, and Opportunities for Microsoft's AI Apps and Services: Focuses on mapping Microsoft's AI ecosystem including Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry Tools to real business use cases, while leveraging built-in scalability, security, and safety benefits.

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Microsoft AI Transformation Leader AB-731 Prüfungsfragen mit Lösungen (Q94-Q99):

94. Frage
Your company is developing an AI-powered customer support agent.
You need to ensure that the solution follows Microsoft responsible AI principles.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Ensure that the agent can be used for multiple purposes.
B. Provide a clear disclaimer that users are interacting with an AI solution.
C. Test the agent to ensure that responses are inclusive and culturally sensitive.
D. Retain all customer conversations.
E. Enable the agent to operate independently.

Antwort: B,C

Begründung:
[B]
Under Microsoft's Responsible AI framework, this testing specifically addresses the principles of Fairness and Inclusiveness. To operationalize these, you should focus on the following testing and development areas:
Key Testing Focus Areas
*-> Cultural Sensitivity: Ensure the agent respects different values, perspectives, and international contexts to avoid causing offense or misunderstanding.
*-> Fairness and Bias Mitigation: Test the agent to ensure it treats all users equitably and does not reinforce societal stereotypes or discriminate based on protected characteristics like race, gender, or religion.
Accessibility: Validate that the agent is usable by people of all abilities, providing equal power and engagement regardless of their background.
Global Community Engagement: Involve diverse users and underserved communities in the pre- deployment validation and feedback process to identify representation gaps.
[E]
To align with Microsoft's Responsible AI principles, providing a clear disclaimer that users are interacting with an AI solution is a core requirement of the Transparency principle.
Under this principle and the Microsoft Responsible AI Standard, developers must ensure that AI systems are understandable and that users are not deceived into believing they are interacting with a human.
Reference:
https://learn.microsoft.com/en-us/legal/ai-code-of-conduct
https://www.microsoft.com/en-us/ai/principles-and-approach

95. Frage
What is considered a best practice when forming an AI adoption team in an enterprise environment?

A. Include only data scientists and engineers at first to validate technical feasibility, then add other stakeholders later.
B. Include procurement and vendor management specialists early to evaluate AI tools, involving business teams once a platform is selected.
C. Include primarily IT and project management staff initially to streamline deployment, adding governance and compliance roles later.
D. Include representatives from legal, leadership, and business units to align AI initiatives with organizational prioritie

Antwort: D

96. Frage
Your company plans to build a generative AI solution based on internal data. You recommend using Microsoft Foundry as a starting point to develop and manage the solution. What is a key benefit of using Microsoft Foundry for this project?

A. Enables business users to build generative AI solutions.
B. Removes the need to select or configure the underlying AI model.
C. Offers a low-code platform for developing generative AI solutions.
D. Provides a scalable platform for developing and deploying generative AI solutions.

Antwort: D

Begründung:
Microsoft Foundry is positioned as a unified, enterprise-grade platform that helps organizations build, deploy, scale, and govern AI apps and agents-especially generative AI solutions that need to work with business context and internal data. That directly aligns with A: Foundry provides a scalable platform for developing and deploying generative AI solutions. Microsoft describes Foundry as an interoperable platform that makes it easier to build, deploy, and scale AI apps and agents, while also providing centralized security and governance features for organizations.
B is incorrect because Foundry does not remove model choice/configuration; in fact, it supports selecting among models and using tools/frameworks to build solutions. You still choose appropriate model(s), configure endpoints, and design grounding and safety controls.
C and D are not the best characterization of Foundry's primary benefit. While Foundry offers "friendly interfaces," Microsoft primarily positions it for developers, model builders, and enterprise AI operations-not as a low-code platform for business users (that role is more commonly filled by Copilot Studio/Power Platform).

97. Frage
Your company uses a non-reasoning generative AI model to create textual content. You discover that the model's responses are inconsistent and do NOT meet expectations. You need to improve the prompts. What should you do? More than one answer choice may achieve the goal. Select the BEST answer.

A. Provide the prompts with extensive examples of the expected output.
B. Add only a single concise requirement to the prompts.
C. Add the context, sources, and expectations to the prompts.
D. Use technical terms in the prompts to enhance AI comprehension.

Antwort: A,C

98. Frage
- For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Answer Area
* Azure Vision in Foundry Tools can extract and analyze key phrases from PDF files. Answer: No
* Azure Vision in Foundry Tools can generate images based on natural language descriptions. Answer:
No
* Azure Document Intelligence in Foundry Tools can be used to automate the processing of invoices and credit notes. Answer: Yes
* No - Azure Vision in Foundry Tools focuses on computer vision tasks such as image analysis and OCR (reading text from images and documents). While it can extract text from scanned PDFs via OCR, key phrase extraction is a natural language processing capability provided by Azure Language in Foundry Tools , not Azure Vision. Key phrase extraction analyzes text to identify main concepts, which is a different service family than vision.
* No - Azure Vision can analyze existing images (for example, generate captions/descriptions of an image), but generating new images from a text prompt is a generative model capability (for example, DALL E through Azure OpenAI/Azure AI Foundry model endpoints), not an Azure Vision feature.
Vision describes what it "sees"; it doesn't synthesize new images from natural language.
* Yes - Azure Document Intelligence in Foundry Tools is designed for intelligent document processing
, including automating extraction of structured fields from financial documents. Microsoft provides prebuilt models for invoices and supports custom extraction for similar document types, which makes it suitable for automating workflows involving invoices and credit-note style documents (field extraction, validation, routing).

99. Frage
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