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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 2: Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 47
You need to persist state by using a durable ID so that the agent automatically reuses the full interaction history across turns and sessions.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use a conversation . A conversation is the durable runtime container that maintains the sequence of interactions associated with a particular case or user flow.
The application can persist the conversation ID together with the support-case ID. When the same case resumes later-even in another application session-the application reuses that conversation ID. The runtime can then continue with the prior interaction history rather than requiring the application to reconstruct it manually.
The stored history can include user messages, assistant responses, tool-call requests, and tool outputs, which is precisely what the question requires.
An agent defines reusable behavior such as the model, instructions, and tools. Many independent conversations can use the same agent, so the agent ID is not the state container for an individual support case.
A response represents an individual model/run result, while an output item is one item produced during response processing. Neither represents the complete durable interaction history.
This separation between durable conversational state and reusable agent configuration is central to Foundry ' s agent runtime model.
Study Guide alignment: define conversation-tracking approaches and build agents that integrate retrieval, function calling, and conversation memory . Microsoft Learn
NEW QUESTION # 48
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 Al 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:
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block . Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
NEW QUESTION # 49
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
Answer: C
Explanation:
Setting the output format parameter to Markdown is the correct and recommended action, but the exact property and enum name depend on whether you are interacting with the REST API directly or using the Python SDK.
To process PDF invoices and preserve their tables, headings, and visual sections in GitHub Flavored Markdown (GFM), configure your Azure Document Intelligence layout model parameters using the precise syntax detailed below.
Implementation Details
Python SDK Syntax: In the Azure Python client library, the parameter name is output_content_format, and its required value is DocumentContentFormat.MARKDOWN (rather than ContentFormat.MARKDOWN, which is used in the .NET C# SDK) Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/markdown-elements
NEW QUESTION # 50
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 51
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: B
NEW QUESTION # 52
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