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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
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NEW QUESTION # 128
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 # 129
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RiskReviewer.
PolicyWriter generates draft updates for customer policies, and RiskReviewer reviews the drafts. In the visual builder, you need to create a workflow that meets the following requirements:
* Finalizes low-risk updates without manual intervention
* Ensures predictable execution across the agents
Answer:
Explanation:
Explanation:
* Orchestration pattern: The sequential template that passes outputs node-by-node
* Approval checkpoints: Add a condition statement
Use the sequential orchestration template because the agents must execute in a fixed, predictable order.
PolicyWriter first generates the proposed policy update, and its output is then passed directly to RiskReviewer. Microsoft defines the sequential pattern as passing the result from one agent to the next in a defined order, making it appropriate for deterministic, multistage processing. The group-chat pattern would allow control to move dynamically between agents, which would reduce execution predictability.
After RiskReviewer returns a structured risk classification, add a condition statement that evaluates the result. The low-risk branch can proceed directly to the finalization action without requesting human input. A separate higher-risk branch can route to an approval step, such as an Ask a question node. Adding an Ask a question node without conditional branching would pause every workflow execution, including low-risk updates, and therefore would not satisfy the automation requirement. Microsoft Foundry workflows support if
/else branching and condition expressions for selecting the next action.
Study Guide alignment: implement orchestrated multi-agent solutions and build autonomous or semiautonomous workflows with safeguards and approval-flow controls .
NEW QUESTION # 130
You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
Answer: C
Explanation:
To deploy the Azure OpenAI DALL-E model, the most appropriate tools to use are Azure AI Studio for model management and interaction, and the Azure Command-Line Interface (CLI) for deployment and configuration tasks. Azure AI Studio provides an interactive interface for working with Azure AI models, and the Azure CLI allows you to manage resources, deployments, and other Azure services from the command line.
NEW QUESTION # 131
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search as the retriever.
You plan to ingest PDF into an Azure AI 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: B
Explanation:
The best configuration step an indexer to extract image data into a normalized_images collection.
Cracking Foundation: In Azure AI Search, document cracking automatically separates textual data from visual content. To make embedded images available to image-processing skills like OCR, the indexer must be explicitly configured with an imageAction configuration property (such as generateNormalizedImages or generateNormalizedImagePerPage).
Input Structure for OCR: This indexer step extracts embedded images from the PDF and restructures them into an internal normalized_images array (accessed via the path
/document/normalized_images/*). The built-in OCR skill strictly requires this normalized image collection format as its input payload.
Incorrect:
[Not C]
Content Field Limitations: The /document/content field generated during document cracking holds only the raw text extracted from the file. It does not contain the binary data or structural properties of embedded images.
Targeting Errors: Pointing an OCR skill directly at the raw text content field would fail to analyze the actual images, leaving the agent unable to ground answers in visual elements like diagrams or embedded infographics.
Reference:
https://docs.azure.cn/en-us/search/tutorial-skillset
NEW QUESTION # 132
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 # 133
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