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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 2: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
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NEW QUESTION # 99
You are developing an app that will use the Azure Vision in foundry Tools API to analyze an image.
You need configure the request that will be used by the app to identify whether an image is explain or a line drawing.
How should you complete the request? To answer, select the appropriate options tn the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an HTTP POST request to the Analyze Image endpoint and request the imageType visual feature.
The image-analysis operation receives image content or an image reference and evaluates the visual features specified in the request. imageType is the feature that supplies information indicating whether an image is clip art or a line drawing.
This is distinct from other feature values shown in the exhibit. description produces a textual image description. Objects performs object detection and returns detected objects and bounding boxes. tags produces semantic labels describing visual concepts. None of those provides the specific clip-art/line-drawing classification requested here.
The pretrained image-type capability eliminates the need to create and train a Custom Vision classifier to distinguish ordinary photographs from drawings. The application can simply examine the returned image-type properties and implement its classification policy.
The use of POST is consistent with submitting image data to the image-analysis endpoint.
The AI-103 Study Guide includes visual understanding workflows and selecting appropriate Foundry
/Vision capabilities for identifying visual characteristics. Microsoft Learn
NEW QUESTION # 100
You need to configure personalized user interactions for Agent1 based on the business requirements. What should you include in the solution?
Answer: A
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 # 101
You are building an image-sharing app that must prevent users from sharing sexually explicit images. The solution must minimize development effort.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Content Safety in Foundry Control Plane Studio because Content Safety provides pretrained harmful- content classifiers for image and text moderation.
Sexual content is one of the supported harmful-content categories. The application can submit an uploaded image for analysis, inspect the resulting category/severity signal, and enforce the organization ' s publishing policy without building a custom image classifier.
This minimizes development effort because there is no requirement to collect sexually explicit and safe training samples, label them, train a model, evaluate iterations, deploy a custom endpoint, or maintain the model over time.
Vision Studio provides general image-analysis capabilities such as OCR and visual understanding but is not the specialized moderation capability for enforcing harmful-content policies. Visual Studio is a development environment rather than an AI service. Microsoft Foundry is the broader development platform; the concrete moderation capability needed by this question is Content Safety.
The application should establish policy thresholds that define when uploaded images are accepted, rejected, or routed to manual review.
The AI-103 Study Guide explicitly covers implementing filters to classify unsafe or disallowed visual content under responsible AI for multimodal content. Microsoft Learn
NEW QUESTION # 102
You need to persist state by using a durable ID so that the agent automatically reuses the full interaction history across turns and sessions.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use a conversation . A conversation is the durable runtime container that maintains the sequence of interactions associated with a particular case or user flow.
The application can persist the conversation ID together with the support-case ID. When the same case resumes later-even in another application session-the application reuses that conversation ID. The runtime can then continue with the prior interaction history rather than requiring the application to reconstruct it manually.
The stored history can include user messages, assistant responses, tool-call requests, and tool outputs, which is precisely what the question requires.
An agent defines reusable behavior such as the model, instructions, and tools. Many independent conversations can use the same agent, so the agent ID is not the state container for an individual support case.
A response represents an individual model/run result, while an output item is one item produced during response processing. Neither represents the complete durable interaction history.
This separation between durable conversational state and reusable agent configuration is central to Foundry ' s agent runtime model.
Study Guide alignment: define conversation-tracking approaches and build agents that integrate retrieval, function calling, and conversation memory . Microsoft Learn
NEW QUESTION # 103
You have a Microsoft Foundry project that contains an agent and uses a GitHub repository. The repository contains a YAM file named File1 that defines the evaluation settings of the agent. You need to create a GitHub Actions workflow that runs the evaluation defined in File1 when a pull request (PR) is opened. How should you configure the workflow?
Answer: A
Explanation:
The correct configuration choice is to set project-endpoint to the endpoint of the project.
azure-ai-project-endpoint: This parameter is required and must be set to the URL endpoint of your Microsoft Foundry project. This aligns with setting project-endpoint.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluation-github-action
NEW QUESTION # 104
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