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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
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NEW QUESTION # 94
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
Answer: C
Explanation:
To resolve the HTTP 403 Forbidden errors when making inference calls via the Azure OpenAI v1 API and DefaultAzureCredential, users must be assigned the Cognitive Services OpenAI User built-in Azure RBAC role.
Why This Happens
Running az login successfully authenticates the user with Microsoft Entra ID, but it does not grant data-plane access permissions. By default, standard control-plane roles (like Reader or Foundry User) only allow users to view project metadata or manage settings, not send prompts to the model deployment endpoint itself.
Recommended Role Definition
Role Name: Cognitive Services OpenAI User
Permissions Granted: This role provides the absolute minimum privileges required to execute chat completions, embeddings, and general inference tasks (Microsoft.CognitiveServices/accounts/OpenAI/deployments/search/action and Microsoft.CognitiveServices/accounts/OpenAI/deployments/causalLanguageModeling/action). It does not allow users to deploy new models, view access keys, or alter configurations Scope Placement: Assign this role to the users (or a Microsoft Entra ID Group) at the Azure OpenAI resource level or the Resource Group level containing your Microsoft Foundry infrastructure.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/managed-identity
NEW QUESTION # 95
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever.
You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
Answer: D
Explanation:
The correct indexing approach is to use an indexer to extract image data into a normalized_images collection . In Azure AI Search enrichment pipelines, embedded images in PDFs are not passed directly from the text content field into OCR. Instead, the indexer must perform document cracking and image extraction by enabling the indexer image action. Microsoft's Azure AI Search documentation states that image-processing skills such as OCR and image analysis expect normalized images, and that enabling imageAction causes embedded images to be extracted and normalized for downstream skills.
The OCR skill is designed to receive image input from /document/normalized_images/*. Microsoft's skillset tutorial specifically states that the OCR skill assumes a normalized_images field exists and that this field is generated by setting the indexer imageAction configuration to generateNormalizedImages. The document extraction skill reference also confirms that generateNormalizedImages creates an array of normalized images during document cracking for OCR and image analysis.
Option A is incorrect because OCR does not run directly against the index content field. Option B maps outputs after enrichment; it does not extract images. Option D reshapes data but does not create the required normalized image collection. Reference topics: Azure AI Search indexers, AI enrichment, OCR skill, imageAction, and normalized_images.
NEW QUESTION # 96
An agent must call your organisation's internal inventory system, which is exposed as a set of tools. You want the tool definitions maintained centrally and reusable across several agents rather than redefined in each agent. Which Foundry capability fits?
Answer: B
Explanation:
A Model Context Protocol (MCP) server exposes a reusable, centrally maintained set of tools that any agent can connect to, and Foundry Agent Service supports adding MCP servers from the tools catalogue. This is the standard pattern for connecting agents to external systems through shared tool definitions.
NEW QUESTION # 97
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 98
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:
The correct answer is D. Set the output_content_format=ContentFormat.MARKDOWN value . Azure Document Intelligence Layout API can return extracted document content in Markdown format, preserving semantic structure such as headings, paragraphs, sections, tables, and other layout elements. Microsoft's Document Intelligence layout guidance shows the Python SDK pattern for analyzing a document with the prebuilt-layout model and setting output_content_format=ContentFormat.MARKDOWN in the begin_analyze_document call. The Markdown output is returned in the top-level content section of the analysis result.
This minimizes development effort because the service produces structure-preserving Markdown directly, rather than requiring custom post-processing to reconstruct sections and table formatting from raw OCR spans. Microsoft's Markdown output documentation states that specifying Markdown output produces semantically structured content that maintains paragraphs, headings, tables, and other document elements in their proper hierarchy.
Option A only changes validation behavior and does not generate Markdown. Option B requests figures, not structured Markdown. Option C uses an incorrect parameter name; the documented SDK setting is output_content_format, not content. Reference topics: Azure Document Intelligence Layout API, Markdown output, PDF analysis, table extraction, and Foundry Tools document processing.
NEW QUESTION # 99
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