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| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 64
You have a Microsoft Foundry project containing an agent and a GitHub repository. The repository contains a YAML file that defines the evaluation settings.
You need a GitHub Actions workflow that runs the evaluation when a PR is opened.
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The workflow needs the Microsoft Foundry project endpoint so that the evaluation action knows which Foundry project and agent environment it should access.
Microsoft ' s current GitHub Actions documentation lists the Foundry project endpoint as a required evaluation input (azure-ai-project-endpoint). The action uses that endpoint together with model/deployment, dataset, and agent information to execute evaluation against the appropriate project. Microsoft Learn The tenant ID is used by the Azure login/authentication step but does not identify the Foundry project to evaluate. A model deployment name identifies a particular deployed model but not the complete project or agent resource. Current Microsoft evaluation actions also do not use an evaluation-config input matching option B as the primary project-binding parameter shown in this question.
The GitHub workflow should additionally be configured with a pull-request trigger and use Microsoft Entra
/OIDC authentication rather than long-lived secrets wherever possible.
This directly matches the AI-103 Study Guide objective to integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines and automate evaluations before release.
Microsoft Learn
NEW QUESTION # 65
Drag and Drop Question
You have a web app that uses Azure AI Search.
When reviewing activity you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Enforces Read-Only Permissions: Query keys are specifically designed to provide read-only access to the documents collection of an index. Admin keys provide full read-write administrative privileges and should never be distributed to consumer-facing applications.
Zero Downtime: Azure AI Search lets you generate up to 50 individual query keys. Creating a new one allows the app to stay online throughout the entire key rotation process Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-api-keys
NEW QUESTION # 66
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
NEW QUESTION # 67
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 # 68
You have an Azure AI agent solution.
You plan to create an agent-based app named App1 that will analyze and summarize data for users and generate data-driven recommendations. App1 will be used by non-technical business users and must adapt to new and unforeseen business challenges and improve its performance over time.
You need to identity which type of agent to use in App1. The solution must meet the following requirements:
- Adapt and improve the agents' performance over time based on user
feedback.
- Provide tailored recommendations to help users make informed
decisions.
- Provide the best possible performance of the app.
Which agent type should you identify?
Answer: B
Explanation:
A cognitive agent is designed to mimic human thought processes, making it ideal for the complex, data-driven tasks described in the query.
Analyze and summarize data and generate data-driven recommendations:
Cognitive agents use machine learning and natural language processing to analyze historical data and act on their learnings, which includes generating insights and recommendations.
Adapt and improve over time based on user feedback:
They are designed to learn and adapt to user behavior and preferences over time, improving their responses and performance through feedback.
Used by non-technical business users:
These agents can be accessed through user-friendly interfaces, often natural language-based (like virtual assistants), making them suitable for non-technical users.
Provide tailored recommendations to help users make informed decisions:
By learning from data and interactions, they can deliver personalized recommendations that support better decision-making.
NEW QUESTION # 69
......
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