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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% | - Build knowledge bases and search solutions
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 74
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: B
Explanation:
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 Requirements
Mask-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.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/image-generation
NEW QUESTION # 75
You have an Azure subscription that contains an Azure OpenAI resource.
You deploy the GPT-4 model to the resource.
You need to ensure that you can upload files that will be used as grounding data for the model.
Which two types of resources should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models.
For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
NEW QUESTION # 76
You need to configure personalized user interactions for Agent1 based on the business requirements. What should you include in the solution?
Answer: D
Explanation:
Agent1 requires memory because the business requirement explicitly states that the agent must retain conversation context and recall relevant information during future interactions. Microsoft Foundry Agent Service memory provides persistent knowledge that can be retained and retrieved across separate conversations, sessions, devices, and workflows. It can store meaningful information extracted from prior interactions, including user preferences, relevant facts, and summarized context, and then retrieve that information when personalizing later responses.
Short-term memory maintains context within the current conversation. Long-term memory supports continuity across future sessions, which directly addresses Contoso's requirement. Memory scopes should be associated with individual user identities so that one customer's stored information cannot be retrieved during another customer's interaction.
Guardrails enforce safety or behavioral restrictions but do not provide conversation recall. Tools enable the agent to access external systems, while instructions define the agent's role and operating boundaries. Neither tools nor instructions persist user-specific information across sessions.
The AI-103 Study Guide maps this requirement to choose appropriate memory, tool, and knowledge integration services and build agents that integrate retrieval, function calling, and conversation memory
.
NEW QUESTION # 77
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used
once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
" tool_choice " : " required "
The correct completion is " tool_choice " : " required " . In Microsoft Foundry Agent Service, tool_choice controls whether the model can answer directly or must invoke a tool during a run. The official Foundry tool guidance states that tool_choice provides deterministic control over tool calling: auto allows the model to decide whether to call tools, none prevents tool use, and required forces the model to call one or more tools.
This directly addresses the issue where the ticket-triage agent sometimes responds without invoking a required tool.
The completed payload should therefore add the tool_choice property beside assistant_id, with the value " required " . The value " auto " is incorrect because it preserves the current nondeterministic behavior. The values " tools " and " type " do not force execution-time tool invocation in this payload; they are used for tool definitions or typed objects in other contexts. response_format controls output formatting, not tool execution.
Reference topics: Microsoft Foundry Agent Service, tool calling reliability, run payload configuration, tool_choice, agent execution, and deterministic tool invocation.
NEW QUESTION # 78
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: A
Explanation:
The correct answer is D. prompt shields for documents . The OCR text is extracted from uploaded images and appended as additional context, so it is third-party content rather than a direct trusted user instruction.
Microsoft Foundry Prompt Shields distinguish between user prompt attacks , which are malicious instructions directly supplied as user prompts, and document attacks , which are hidden or embedded instructions in third-party content such as documents, emails, webpages, or grounded data. Microsoft also notes that Prompt Shields analyze indirect attacks embedded in input documents or images.
This scenario is an indirect prompt injection pattern: an uploaded screenshot may contain text that says to ignore the system prompt or perform unauthorized actions. Because the OCR output is being passed to the multimodal model as contextual content, the appropriate protection is Prompt Shields for documents, which scans externally sourced context for malicious embedded instructions before generation. Protected material text detects known copyrighted text in model outputs, not prompt injection. Image moderation detects harmful visual content categories, not malicious instructions in extracted context. Prompt Shields for user prompts are less precise here because the risk comes from document-like content derived from the uploaded image.
Reference topics: Prompt Shields, document attacks, indirect prompt injection, multimodal safety, OCR- derived context, and Foundry guardrails.
NEW QUESTION # 79
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