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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI solutions by using Microsoft Foundry | 55-60% | - Get started with Microsoft Foundry
|
| Topic 2: Identify AI concepts and responsibilities | 40-45% | - Describe principles of responsible AI
|
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NEW QUESTION # 65
You have a Microsoft Foundry project that contains a vision-enabled GPT model deployment.
You need to develop an application that sends an image and a text question to the model in a single request.
What should you use?
Answer: B
Explanation:
To send an image and a text question to a vision-enabled GPT model deployment within a Microsoft Foundry project in a single request, you should target the Azure OpenAI Responses API via your Foundry Project Endpoint.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/gpt-with-vision
NEW QUESTION # 66
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.
Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement.
Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system's output. = Yes Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.
NEW QUESTION # 67
You deploy an AI system to assist with hiring decisions.
Company policy requires that human reviewers oversee AI-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible AI principle is this an example of?
Answer: D
Explanation:
Accountability is the Microsoft responsible AI principle that ensures human oversight so that people remain responsible for AI-driven decisions.
Human Oversight: Requiring human review ensures that AI operates under effective governance rather than acting autonomously on high-impact choices.
Final Authority: Keeping humans legally and operationally responsible prevents automated systems from dictating critical outcomes like hiring.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 68
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
An AI workload that produces new content based on user input is an example of generative AI.
Generative AI is used to create new content, such as text, images, code, audio, or other outputs, from user prompts or instructions.
The other options are incorrect:
content understanding extracts or interprets information from existing content.
information extraction identifies and extracts structured data from content.
text analysis analyzes existing text for entities, sentiment, key phrases, language, or summaries.
NEW QUESTION # 69
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: D
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema . In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms.
Microsoft's quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.
Why the other options are incorrect:
A). transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.
B). OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.
D). Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.
Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools .
NEW QUESTION # 70
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