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| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 2: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Topic 3: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 5: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
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NEW QUESTION # 86
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: B
Explanation:
The first step should be an Azure Content Understanding analyzer within Microsoft Foundry Tools.
This specific tool is designed to handle the complexities in this scenario--scanned text, complex tables, and multi-column layouts--by converting unstructured documents into a structured Markdown format. This preserves the document's original hierarchy and structural relationships, which is critical for accurate downstream reasoning by an agent.
Incorrect:
[Not A]
Azure Language is designed for text-based natural language processing (NLP) like sentiment analysis or PII redaction on raw text strings. It cannot parse scanned images, optical character recognition (OCR), tables, or complex multi-column visual layouts.
[Not C]
The first step in this specific Microsoft Foundry agent configuration should not be an Azure OpenAI Responses API call.While multimodal models can read images, they frequently hallucinate data, fail to capture complex multi-column reading orders, and degrade table structures when processing dense, mixed-format documents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/elements
NEW QUESTION # 87
You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?
Answer: D
Explanation:
The best solution in this scenario is to implement image moderation.
Azure AI Content Safety provides dedicated image moderation capabilities that automatically detect, classify, and score harmful visual content (such as violence, hate speech, sexual content, and self-harm). It natively allows you to configure threshold blocklists based on four severity levels (Low, Medium, High), perfectly matching the requirements.
Incorrect:
[Not A]
Optical Character Recognition (OCR) only extracts text embedded within an image. If a user uploads a harmful photo that contains no text (e.g., a violent image), OCR and keyword scanning will completely fail to detect it.
[Not B]
The Prompt shields feature is designed to protect generative AI models from text-based attacks, such as jailbreaking or prompt injection. It does not analyze or classify the visual pixels of an uploaded photo for harmful content.
[Not C]
While Azure AI Content Safety uses blocklists, standard text blocklists match specific exact terms or regex patterns. They are used for text moderation, not for evaluating the severity levels of raw visual imagery.
Reference:
https://supportzebra.com/blog/what-is-ai-image-moderation
NEW QUESTION # 88
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
- Each workflow run must include a retrieval step before generating a
response.
- Tool calls must authenticate by using the published agent's own
identity.
- Tool access must use an identity isolated from other project
resources.
- Tool access must use support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 89
Drag and Drop Question
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 90
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a deployed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
NEW QUESTION # 91
......
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