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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 89
A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?
Answer: B
Explanation:
Prompt Shields detects prompt injection attacks, including indirect injection where malicious instructions are embedded in content the model processes, such as text hidden inside an image.
NEW QUESTION # 90
You have a Microsoft Foundry project named Project1 that contains the following:
* An OpenAPI tool that calls an external API
* A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?
Answer: B
Explanation:
The correct action is to connect the OpenAPI tool to Connection1 . In Microsoft Foundry Agent Service, an OpenAPI tool does not automatically use every project connection in the project. For API key authentication, the tool must be explicitly configured to use the project connection that contains the required secret.
Microsoft's OpenAPI tool guidance states that API key or token authentication requires a project connection configured with the API key or token, and the tool is then created or configured to use that connection for authentication. The documentation also notes that the agent calls the external API by using the stored API key when the OpenAPI tool is configured with the project connection.
Option A is incorrect because a project's default connection is not automatically the authentication source for a specific OpenAPI tool. Option C is incorrect because identity passthrough or managed identity only applies when the target API accepts Microsoft Entra ID tokens; this scenario uses an external API key. Option D is also incorrect as a credential value should not be manually embedded in the OpenAPI specification. The specification defines the security scheme, while the secret value is stored in the Foundry connection.
Reference topics: OpenAPI tools, project connections, API key authentication, tool authentication configuration, and agent tracing.
NEW QUESTION # 91
You have an invoice-processing application named App1 that uses Azure Content Understanding in Foundry Tools. You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
* Compare an invoice to its related purchase order.
* Validate the invoice against static vendor contract documents.
* Return a single structured output that includes discrepancy findings.
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?
Answer: B
Explanation:
Configure a multi-file task in pro mode . At analysis time, provide the invoice and its related purchase order as the input documents. During analyzer creation, add the static vendor contracts as reference data . Pro mode can reason across multiple input documents and use reference documents as contextual knowledge for validation, enrichment, and discrepancy detection. Microsoft specifically documents the scenario of supplying an invoice and purchase order as inputs while using contract files as reference data to identify inconsistencies.
Define a field schema containing the required invoice information and generated discrepancy findings.
Building the task creates an analyzer ID that applications invoke through one analyzer API endpoint, producing a unified structured result governed by that schema.
Standard mode is optimized for straightforward processing of individual files. It does not support cross-file analysis, reference-dataset integration, or the multi-step reasoning required to compare invoices, purchase orders, and contractual conditions. A "multi-file task in standard mode" is therefore not a supported configuration. Confidence scores do not address document comparison and, notably, are not available in pro mode.
Study Guide alignment: configure Content Understanding analyzers, design structured schemas, process multiple documents, integrate reference data, and implement document validation workflows .
NEW QUESTION # 92
Hotspot Question
You have a Microsoft Foundry project.
- You need to create a customer support agent that meets the following
requirements:
- Grounds responses only in company policy documents stored in curated
repositories
- Retains customer preferences across separate chat sessions
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Configure retrieval from approved data sources
You should configure retrieval from approved data sources to meet this requirement.
In Microsoft Foundry (and broader Azure AI Foundry / Azure OpenAI architectures), grounding an agent exclusively in company policy documents requires implementing a Retrieval-Augmented Generation (RAG) pattern. This ensures the AI model only answers using the provided context and does not hallucinate or rely on its public training data.
Box 2: Enable agent memory that uses persistent storage
To retain user preferences across completely separate chat sessions or conversations, you must use persistent agent memory. Traditional chat history only tracks messages within a single active thread or session. By contrast, the Memory Store feature provides a managed, long-term memory system that structurally indexes user preferences and facts across different devices and separate sessions.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-capabilities-for-azure-openai-on-your-data/4144636
NEW QUESTION # 93
Hotspot Question
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure AI Content Safety must access the images by using the blob URL.
The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 94
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