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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
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NEW QUESTION # 32
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Answer: C
Explanation:
A vision-enabled Chat Completions request must represent the user message as a multimodal content array.
One item contains the prompt with " type " : " text " , while another contains the image reference with " type ": " image_url " and an image_url.url property:
" content " : [
{ " type " : " text " , " text " : " Analyze this image. " },
{ " type " : " image_url " , " image_url " : { " url " : " < image-url > " }}
]
This structure explicitly identifies each modality, allowing the deployed model to route the text and image through the appropriate processing paths. A URL embedded in an ordinary string remains text and is not interpreted as visual input. Microsoft's documented Chat Completions schema shows the text and image objects inside the user message's content array.
Request metadata does not define model input, and a system message should contain behavioral instructions rather than the user's image attachment. Base64 images are supported, but the encoded value must be formatted as a data URL and supplied through the structured image item-not inserted into a plain content string.
Study Guide alignment: deploy and consume multimodal models, integrate generative workflows into applications, and design multimodal-understanding workflows .
NEW QUESTION # 33
You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?
Answer: A
Explanation:
To configure end-to-end visibility into each step of the request workflow, you should Enable application tracing in the project.
Project-Level Observability: Enabling application tracing allows Microsoft Foundry to natively track, trace, and monitor every component of the workflow-including calls to the Azure OpenAI Responses API and associated tools like Azure AI Content Safety-within the same request without requiring complex, separate SDK configurations.
Centralized Tracking: Tracing captures the exact execution path across the project endpoints, allowing you to see metrics, latency, and inputs/outputs sequentially for both the model invocation and the content safety check.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/trace-production-sdk
NEW QUESTION # 34
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support a search solution for internal policy documents. The model must generate vector representations of the text in the documents and of user queries.
Which type of model should you use?
Answer: B
Explanation:
To fulfill this requirement, you need an embeddings model from the Microsoft Foundry AI Model Catalog that captures semantic meaning and outputs numerical vectors for text chunks.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/concepts/understand-embeddings
NEW QUESTION # 35
Drag and Drop Question
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
- Ensure that the agent can perform calculations during conversations.
- Ensure that the agent can access up-to-date information from public
websites.
- Ensure that the agent can retrieve information from documents
uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements. Each tool 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 # 36
You have a Microsoft Foundry project that contains three agents as shown in the following table.
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
- Support a deterministic, step-based process that uses conditional
branching and shared state across the agents.
- Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: B
Explanation:
To fulfill your requirements while keeping development effort to an absolute minimum, you should leverage the native Microsoft Foundry Multi-Agent Workflows feature (built directly into the Foundry Agent Service and managed via the Foundry portal visual editor or declarative YAML files).Using this visual, low-code orchestration layer removes the need to write custom graph routing logic, state managers, or manual handoffs in code.
The minimum required architecture and features that must be included in your solution are structured below.
1. The Orchestration Layer: Declarative Workflow
Instead of writing a code-first orchestrator, you must define a Foundry Workflow Definition (YAML or Visual).
2. State Management: Shared Context Variables
3. Agent Configuration & Native Tooling
Reference:
https://devblogs.microsoft.com/foundry/introducing-multi-agent-workflows-in-foundry-agent-service/
NEW QUESTION # 37
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