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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
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NEW QUESTION # 19
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 # 20
Hotspot Question
You are creating an enrichment pipeline that will use Azure AI Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Object projection
For the unstructured JSON data, you must use object projection, and for the extracted text data from the scanned PDF documents, you must use file projection.
Object Projection: Projects your data as a full JSON representation. It is ideal for maintaining the original structure alongside any applied AI enrichments within a single JSON document inside Azure Blob Storage.
Box 2: File projection
File Projection: Captures the binary or image extraction layer required during Optical Character Recognition (OCR) processing. It isolates and outputs raw text and graphic details directly from unstructured physical binary files into a designated container Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-concept-image-scenarios
NEW QUESTION # 21
You have a Microsoft Foundry project. You need to deploy a model from the model catalog to support real- time inference. The solution must meet the following requirements:
* Use key-based authentication
* Support real-time REST API access
* Not consume the vCPU quota of the virtual machines in the Azure subscription Which type of deployment should you use?
Answer: D
Explanation:
A serverless API deployment exposes a model from the Microsoft Foundry model catalog as a managed inference endpoint without hosting the model on virtual-machine compute in the customer's subscription.
Consequently, it does not consume the subscription's VM-family vCPU quota. Capacity is managed by the service and controlled through deployment-level token and request rate limits rather than customer-managed compute instances.
The generated serverless endpoint uses key authentication. Foundry provides a target URI and associated primary or secondary credentials that the application uses to authorize inference requests. Serverless deployments support the Azure AI Model Inference API, enabling real-time application access through a consistent API interface suitable for REST-based prediction requests.
A self-hosted container requires customer-managed infrastructure and therefore consumes the compute capacity on which it runs. A compute-backed standard deployment generally requires provisioned hosting resources and applicable quota. A batch deployment is designed for asynchronous, high-volume processing rather than interactive real-time inference.
The serverless option therefore satisfies all three requirements simultaneously: key-based authorization, online API inference, and no dependency on the subscription's virtual-machine vCPU quota.
Study Guide alignment: Planning and managing Azure AI solutions - select model deployment options, deploy catalog models, configure endpoint authentication, and evaluate compute and quota requirements.
NEW QUESTION # 22
You have an Azure subscription that contains an Azure Al Foundry instance named AI1.
You have an app that automatically triages and resolves issues presented in the log files of a system.
You create an incident manager agent and a DevOps agent that collaborate to resolve the issues.
You need to ensure that the incident manager agent can assign work to the DevOps agent. The solution must minimize development effort.
What should you do?
Answer: C
Explanation:
Ensuring that an incident manager agent can assign work to a DevOps agent in Azure AI Foundry is primarily achieved through a Connected Agents configuration. This setup allows a "main" agent to delegate tasks to "specialized" agents via natural language or defined function calls.
Key Implementation Steps
1. Configure Connected Agents
Within the Azure AI Foundry portal, you must register the DevOps agent as a "Connected Agent" of the incident manager.
2. Define Tool-Based Delegation
Use the ConnectedAgentToolDefinition in the Azure Python SDK or C# SDK to programmatically link them. This exposes the DevOps agent to the incident manager as a callable "tool".
3. Implement Handoff Orchestration Patterns
Choose an orchestration pattern that fits the triage-to-resolve workflow.
4. Manage Context and State
5. Enable Permissions and Roles
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/connected-agents
NEW QUESTION # 23
You are defining an agent in Microsoft Foundry Agent Service. The agent uses a catalogue model for reasoning, a system instruction that sets its goals, and a file search capability that reads a knowledge store. Which three components does this combination represent?
Answer: C
Explanation:
Microsoft Foundry Agent Service defines every agent as the combination of three core components: a model that provides reasoning, instructions that set goals and behaviour, and tools that give access to data or actions. The scenario maps exactly, with the catalogue model, the system instruction, and the file search tool.
NEW QUESTION # 24
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