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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
>> Actual AI-103 Test Answers <<
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NEW QUESTION # 98
You need to configure Agent1 to meet the security and compliance requirements.
What should you use?
Answer: A
NEW QUESTION # 99
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project.
User messages can contain both text and images.
Currently, App1 includes image URL: as plain text inside the message content so the model cannot recognize them as images.
Traces show that the requests contain a single text message instead of a multimodal content array.
You need to send the message as a structured array that includes both the text portion and the image reference to ensure that the model can process the image correctly.
What should you do?
Answer: C
Explanation:
To fix this, you must change your request payload structure from a single string to a structured content array. Multimodal models in Azure AI Foundry expect an array of distinct objects for text and images.
Reference:
https://towardsdatascience.com/multimodal-ai-search-for-business-applications-65356d011009/
NEW QUESTION # 100
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Authentication method: An Azure Login action that uses OpenID Connect (OIDC) If the evaluation results are NOT met, configure the workflow to: Fail The correct authentication method is Azure Login with OpenID Connect (OIDC) . Microsoft Foundry's GitHub Actions evaluation guidance recommends Microsoft Entra ID authentication and states that authentication can be automated by using the Azure Login GitHub action with OpenID Connect. The sample evaluation workflow also grants id-token: write, runs azure/login@v2, and then invokes the Microsoft AI Agent Evaluation action. This is the appropriate CI/CD authentication pattern because it avoids long-lived personal access tokens and supports secure federated authentication from GitHub Actions into Azure.
The workflow should be configured to fail when evaluation thresholds are not met. Foundry's evaluation GitHub Action is designed to automate pre-production assessment of Microsoft Foundry agents in CI/CD pipelines and produce evaluation results for the configured evaluators and test dataset. A failed GitHub Actions check can then be enforced through branch protection so the PR cannot merge until the quality gate passes. Locking the target branch or sending an alert does not directly implement a CI quality gate. Reference topics: Microsoft Foundry agent evaluation, GitHub Actions evaluation workflow, Microsoft Entra authentication, Azure Login with OIDC, pull-request quality gates, and CI/CD governance.
NEW QUESTION # 101
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
* Each workflow run must include a retrieval step before generating a response.
* Tool calls must authenticate by using the published agent's own identity.
* Tool access must use an identity isolated from other project resources.
* Tool access must support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Set tool_choice to: required
Configure the tool to authenticate by: Using a distinct agent identity bound to the client application Set tool_choice to required because the compliance workflow must deterministically include a tool-based retrieval step before the agent generates a response. Microsoft Foundry Agent Service guidance states that tool_choice provides the most deterministic control over tool use: auto lets the model decide, none prevents tool calls, and required forces the model to call one or more tools. This directly corrects the current nondeterministic behavior where the model decides whether to call tools.
For authentication, use a distinct agent identity bound to the client application . Microsoft Foundry creates a shared identity for unpublished or in-development agents, but publishing an agent automatically creates a dedicated agent identity blueprint and agent identity associated with the agent application resource. Published agents authenticate tool calls by using that unique agent identity, and RBAC permissions must be assigned to the new identity. This provides isolation from the broader shared project identity and supports independent audit trails for compliance workflows.
Storing API keys in prompts violates security guidance and prevents robust audit attribution. The shared project agent identity is easier for development, but it has a broader blast radius and does not meet the isolation requirement. Reference topics: Foundry Agent Service tool choice, tool authentication, published agent identities, RBAC, and auditability.
NEW QUESTION # 102
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a deployed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
NEW QUESTION # 103
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