AI-103学習ガイドは世界を対象としており、ユーザーは非常に広範囲です。ユーザーにより良い体験を提供するために、私たちは常に改善しています。 AI-103試験準備の高い品質と効率は、ユーザーに認められています。当社のAI-103テスト資料の高い合格率は最大の特徴です。 AI-103試験準備を使用している限り、必要なものを確実に収集できます。最短時間でAI-103試験に合格できるだけでなく、夢のあるAI-103認定資格を取得して将来を明るくすることもできます。
| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 2: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 3: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 4: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
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質問 # 119
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 URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
正解：A
解説：
A vision-enabled Chat Completions request must represent the user message as a multimodal content array.
One item contains the prompt with " type " : " text " , while another contains the image reference with " type "
: " image_url " and an image_url.url property:
" content " : [
{ " type " : " text " , " text " : " Analyze this image. " },
{ " type " : " image_url " , " image_url " : { " url " : " < image-url > " }}
]
This structure explicitly identifies each modality, allowing the deployed model to route the text and image through the appropriate processing paths. A URL embedded in an ordinary string remains text and is not interpreted as visual input. Microsoft's documented Chat Completions schema shows the text and image objects inside the user message's content array.
Request metadata does not define model input, and a system message should contain behavioral instructions rather than the user's image attachment. Base64 images are supported, but the encoded value must be formatted as a data URL and supplied through the structured image item-not inserted into a plain content string.
Study Guide alignment: deploy and consume multimodal models, integrate generative workflows into applications, and design multimodal-understanding workflows .
質問 # 120
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
正解：C
質問 # 121
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.
正解：
解説：
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.
質問 # 122
You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
質問 # 123
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
正解：A
解説：
To monitor and control access to a custom agent in Azure, you must first create a Microsoft Foundry project. Once the project is created, you register your custom agent within it to enable management capabilities such as access control and activity monitoring.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/control-plane/register-custom-agent
質問 # 124
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