You are creating an image-editing workflow in a Microsoft Foundry project.The workflow must meet the following requirements:▪ Ensure that background objects can be removed by applying a mask-based inpainting edit.▪ Preserve the original lighting and style of the edited images.▪ Use the built-in image editing controls, NOT a custom model.You need to ensure that image edits apply exclusively inside the masked area.How should you configure the workflow?
Answer(s): D
You should enable mask_inpainting and supply both the input image and the mask to meet all the criteria for your Microsoft Foundry image-editing workflow.By utilizing Microsoft Foundry’s built-in Image Generation Tool parameters, configuring the workflow this way ensures the desired edits are perfectly executed.Workflow Configuration RequirementsMask-Based Object Removal: Passing the mask parameter explicitly flags the exact background object regions targeted for removal, replacing them seamlessly.Preserving Style and Lighting: Enabling mask_inpainting prompts the underlying built-in model (such as gpt-image-2) to inherit and maintain the exact lighting, textures, and style of the surrounding unmasked environment.Built-In Controls Only: This is entirely handled natively via the Foundry Agent Service API variables without deploying a single line of custom code or third-party model checkpoints.Targeted Area Enforcement: The system relies on the provided mask array to ensure pixels outside the marked coordinates are kept completely untouched and protected from VAE degradation.
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/image-generation
You have a Microsoft Foundry project that generates product marketing images from text prompts.After publishing several images, the legal team at your company identifies a competitor’s logo on a sign in the background of an image.You need to remove only the logo, while preserving the rest of the image.What should you do?
Answer(s): A
To remove a logo from a generated image while preserving the rest of the content, you should use AI-powered inpainting tools or standard object removal features in professional image editing software.Microsoft Designer or Azure AI (In-Engine Fixes)If your Foundry project is built on top of Azure OpenAI Service or Dall-E, you can often handle this programmatically or through related first-party tools.Generative Erase / Inpainting: Use Microsoft Designer's "Erase" tool. Brush over the logo, and the AI will replace it by seamlessly blending the background.API Inpainting: If you have access to the underlying image generation API (like DALL-E 3 or Stable Diffusion),use the Inpainting API. Pass the original image along with a black-and-white mask highlighting the logo, and prompt it to "fill the background naturally."
https://starryai.com/en/blog/mai-image-1
You have a Microsoft Foundry project that contains an agent.The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes.The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables.You plan to call Azure Content Understanding in Foundry Tools.You need to extract content and layout elements and detect QR codes without requiring a language model deployment.Which built-in analyzer should you use?
To extract content, preserve tables and document layout, and detect embedded QR codes without deploying a large language model (LLM), you should use the built-in prebuilt-layout analyzer.Note:Unlike schema-driven extraction models in Content Understanding that utilize generative AI orchestration, theLayout analyzer is a highly efficient machine-learning-based model. It natively outputs structural geometry and decodes barcodes without requiring an active LLM deployment or provisioned throughput.Structural Preservation: It extracts headers, paragraphs, and nested sections, returning precise spatial bounding boxes for every single element. Downstream agents can utilize this geometric metadata to anchor or cross-reference sections accurately.Advanced Table Mapping: It maps intricate, multi-page invoice tables, capturing text alongside row and column indices. You can configure the output structure format natively into Markdown or HTML tables to maintain formatting cleanliness.Built-in QR and Barcode Decoding: By default, the configuration parameter enableBarcode is set to true. The analyzer scans the scanned PDF image, isolates 2D code regions, and appends the decoded string payload into the output JSON alongside text blocks.Zero LLM Dependency: It does not route text to foundational models like GPT-4o for its extraction, keeping processing latency low and lowering operational costs significantly.
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/content-understanding-st udio
You have a Microsoft Foundry project that contains an agent and an image generation model deployment.The agent generates original images from user-supplied product photos.You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.What should you do?
You should set the input_fidelity parameter to "high" in this scenario.In Microsoft Foundry (built on Azure AI Foundry), the input_fidelity parameter specifically dictates how strictly the image generation model adheres to the visual features, layout, and style of the source image.Why "high" is the Correct ChoicePreserves Product Identity: Setting input_fidelity="high" ensures that critical branding details, logos, structures,and product contours are preserved exactly as they appear in the original photo.Designed for E-commerce: Microsoft explicitly designed this setting for e-commerce, commercial marketing assets, and product photography to prevent the model from hallucinating or altering key product characteristics.Model Support: This parameter is natively supported when configuring agent image generation tools (such as the ImageGenerationTool or ImageGenTool classes in the Foundry Agent Service) leveraging advanced models like GPT-image-1 or GPT-image-2.
https://developers.openai.com/cookbook/examples/generate_images_with_high_input_fidelity
HOTSPOT (Drag and Drop is not supported)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.Hot Area:
To prevent indirect prompt injection from malicious embedded text in user-uploaded screenshots, the PromptShield action must be set to "block" and Spotlighting should be enabled.Box 1: Set action to blockPrompt shield actionRecommended Azure AI Foundry ConfigurationTo handle untrusted, third-party content within a Microsoft Foundry agent using a multimodal chat model,configure your Guardrails and Content Filters as follows:1. Risk Dropdown Selection: Select Document attack. Document attacks target hidden instructions embedded in external content like documents, images, or files.*-> 2. Prompt Shield Action: Set the action to Block. While "Annotate" flags the vulnerability for downstream handling, "Block" stops the injection attempt in real-time before the text reaches the model session.Box 2: Enable spotlightingAdditional mitigation3. Spotlighting Toggle: Enable Spotlighting. This technique transforms untrusted text to give the model a continuous signal of its source. It treats the extracted screenshot text as lower trust relative to the system prompt, preventing hidden commands from executing.
https://techcommunity.microsoft.com/blog/-/better-detecting-cross-prompt-injection-attacks-introducing/4458404
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