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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Topic 2: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
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NEW QUESTION # 115
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Prompt shields action: Set action to block.
Additional mitigation: Enable Spotlighting.
The correct configuration is to set the Prompt Shields document attack action to block and enable Spotlighting . Prompt Shields in Microsoft Foundry are designed to detect attempts to manipulate model behavior through adversarial input. Microsoft distinguishes document attacks from direct user prompt attacks:
document attacks are malicious instructions embedded in third-party content such as documents, webpages, emails, or other externally supplied material. In this scenario, the embedded text inside uploaded screenshots is third-party content and can attempt to override the agent's instructions. Setting the action to block prevents detected document-attack content from being processed normally, which is required because the goal is prevention rather than passive logging or annotation.
Spotlighting is the additional mitigation because it marks or transforms document content so the model treats it as lower trust than system and user instructions. Microsoft's Foundry guidance describes Spotlighting as a Prompt Shields subfeature that helps protect against indirect or embedded document attacks by tagging input documents with special formatting to indicate lower trust. A custom blocklist is insufficient for unknown attacks, and OCR alone only extracts the malicious text; it does not mitigate prompt injection. Reference topics: Prompt Shields, document attacks, guardrails, Spotlighting, multimodal safety, and prompt injection defense.
NEW QUESTION # 116
You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
- Complex questions must retrieve information from multiple chunks.
- Multi-turn conversations must influence retrieval planning.
- Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?
Answer: D
Explanation:
An agentic Retrieval-Augmented Generation (RAG) architecture is the best fit for your requirements. Traditional, linear RAG pipelines struggle with complex, multi-turn dependencies, whereas an agentic approach natively solves them through iterative reasoning and tool orchestration.
Here is how an agentic RAG pattern specifically satisfies each the three technical requirements:
1. Complex Questions & Multi-Chunk Retrieval
The Challenge: Complex questions often require aggregating distinct pieces of information scattered across different documents or sections.
The Agentic Solution: An agent uses a Reasoning and Acting (ReAct) loop. It evaluates the user's query, breaks it down into sub-questions, and executes multiple distinct search queries. It then synthesizes the information from these diverse chunks before generating a final response.
2. Multi-Turn Conversations & Retrieval Planning
The Challenge: Standard RAG often just passes the latest user message or a simple chat history summary to the search index, which can lose context or misinterpret the user's true intent in long conversations.
The Agentic Solution: The agent acts as a query planner. It maintains the state of the conversation and dynamically decides if a retrieval is needed, what specific keywords or vectors to target based on past turns, and how to reformulate the query to bridge context gaps.
3. Parallel Retrievals for Low Latency
The Challenge: Sequential lookups drastically increase time-to-first-token (TTFT), harming the chat user experience.
The Agentic Solution: Modern AI agent frameworks allow the LLM to emit multiple tool calls simultaneously. The application layer can intercept these calls and execute parallel asynchronous queries against your Azure AI Search vectorized index, drastically minimizing latency.
Reference:
https://arxiv.org/html/2501.09136v1
NEW QUESTION # 117
You have an Azure subscription that contains an Azure AI Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure AI Content Understanding project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?
Answer: C
Explanation:
Cognitive Services Contributor
This role is typically granted access at the resource group level for a user in conjunction with additional roles. By itself this role would allow a user to perform the following tasks.
* Create new Azure OpenAI resources within the assigned resource group.
* Etc.
To create an Azure AI Content Understanding project on an Azure AI Foundry hub, you need either the Azure AI Account Owner, Contributor, or Cognitive Services Contributor role at the subscription level to create the necessary Azure AI Foundry resource, and then the user must have the Azure AI User role at the project level to create the agent within that project.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control
NEW QUESTION # 118
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to recommend a solution to assess the responses generated by Agent1 when the agent uses the product information stored in storage1. The solution must meet the technical requirements. What should you include in the recommendation?
Answer: B
Explanation:
The best way for the Foundry Agent to access the product information stored in the Azure Blob Storage account is by connecting an Azure AI Search index as an indexed knowledge source via the Agent's file search tool.
This implementation creates a robust Retrieval-Augmented Generation (RAG) framework, ensuring the agent retrieves grounded, high-relevance product information.
Scenario:
Technical Requirements;
*-> Responses generated by using the product sheet information must be relevant, complete, and accurate.
Data environment: The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
Planned changes: Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Reference:
https://learn.microsoft.com/en-nz/answers/questions/5868427/uploading-data-in-microsoft-foundry-agent
NEW QUESTION # 119
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 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:
Explanation:
Set tool_choice to: required
Configure the tool to authenticate by: Using a distinct agent identity bound to the client application Set tool_choice to required because the compliance workflow must deterministically include a tool-based retrieval step before the agent generates a response. Microsoft Foundry Agent Service guidance states that tool_choice provides the most deterministic control over tool use: auto lets the model decide, none prevents tool calls, and required forces the model to call one or more tools. This directly corrects the current nondeterministic behavior where the model decides whether to call tools.
For authentication, use a distinct agent identity bound to the client application . Microsoft Foundry creates a shared identity for unpublished or in-development agents, but publishing an agent automatically creates a dedicated agent identity blueprint and agent identity associated with the agent application resource. Published agents authenticate tool calls by using that unique agent identity, and RBAC permissions must be assigned to the new identity. This provides isolation from the broader shared project identity and supports independent audit trails for compliance workflows.
Storing API keys in prompts violates security guidance and prevents robust audit attribution. The shared project agent identity is easier for development, but it has a broader blast radius and does not meet the isolation requirement. Reference topics: Foundry Agent Service tool choice, tool authentication, published agent identities, RBAC, and auditability.
NEW QUESTION # 120
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