弊社は強力な教師チームがあって、彼たちは正確ではやくて例年のMicrosoft AI-103認定試験の資料を整理して、直ちにもっとも最新の資料を集めて、弊社は全会一緻で認められています。Microsoft AI-103試験認証に合格確率はとても小さいですが、ShikenPASSはその合格確率を高めることが信じてくだい。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
最近、Microsoft AI-103試験に合格するのは重要な課題になっています。同時に、AI-103資格認証を受け入れるのは傾向になります。AI-103試験に参加したい、我々ShikenPASSのAI-103練習問題を参考しましょう。弊社は1年間の無料更新サービスを提供いたします。あなたがご使用になっているとき、何か質問がありましたらご遠慮なく弊社とご連絡ください。
質問 # 139
You have a Microsoft Foundry resource named Al1 that hosts three deployments of the GPT 3.5 model. Each deployment is optimized for a unique workload.
You plan to deploy three apps. Each app will access AM by using the REST API and will use the deployment that was optimized for the apps intended workload.
You need to provide each app with access to All and the appropriate deployment. The solution must ensure that only the apps can access AH.
What should you use to provide access to AI1. and what should each app use to connect to its appropriate deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Use Microsoft Entra ID authentication and pass an OAuth 2.0 bearer token in the REST request's Authorization header. Assign the required inference role, such as Cognitive Services User , only to each application's managed identity or service principal. This authenticates the applications as distinct security principals and supports resource-scoped RBAC, auditing, token expiration, and immediate access revocation.
Microsoft recommends Entra ID for production workloads because API keys are static, resource-wide secrets that cannot identify individual callers or provide granular authorization.
Each application must specify its appropriate deployment name when making an inference request. Foundry deployments assign a unique name and configuration to each deployed model. In the deployment-level REST API, that name appears in the request route:
/openai/deployments/{deployment-name}/chat/completions
The resource endpoint and authentication mechanism can remain common across all three applications, while the deployment name directs each request to the GPT-3.5 deployment optimized for that application's workload. A deployment type describes capacity or provisioning characteristics and does not select a particular deployed model.
Study Guide alignment: configure authentication, managed identities, RBAC, model deployments, and REST- based model consumption.
質問 # 140
Your company is piloting a customer support agent in a Microsoft Foundry project name Project1. Project1 is connected to an existing Application Insights resource, and the company ' s support team reviews runs in the Traces tab.
The Foundry Agent Service is configured to perform the following actions:
* Retrieve the Application Insights connection string by calling
project_client.telemetry.get_application_insights_connection_string().
* Call configure_azure_monitor(connection_string=...) to enable telemetry.
A separate LangChain service configured to use OpenTelemetry and has the following configurations:
* Uses AzureAIOpenTelemetryTracer(connection_string=..., enable_content_recording=False)
* Passes the tracer by using config={ " callbacks " :[azure_tracer]}
Company policy has the following requirements:
* Telemetry from LangChain and OpenTelemetry must be distinguishable within the same Application Insights resource.
* Secrets and credentials must NOT be stored in prompts, tool arguments, or span attributes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
The LangChain service will appear in Traces without configuring a tracer: No Setting different OTEL_SERVICE_NAME values separates the services in Application Insights: Yes When using enable_content_recording=False, prompts and tool data will be captured in the telemetry: No The first statement is No because a separate LangChain or LangGraph application must emit telemetry through the configured tracing integration. Microsoft's LangChain tracing guidance states that you configure AzureAIOpenTelemetryTracer, attach it to the runnable or agent through callbacks, and then inspect the emitted traces in Azure Monitor. The troubleshooting guidance also states that missing LangChain or LangGraph spans are caused by tracing callbacks not being attached to the run.
The second statement is Yes . In OpenTelemetry, OTEL_SERVICE_NAME maps to the service.name resource attribute. Azure Monitor Application Insights uses cloud role names to represent separate services, and Microsoft states that when multiple services emit to the same Application Insights resource, cloud role names must be set so services are represented properly.
The third statement is No . enable_content_recording=False is specifically used to redact message content and tool call arguments from traces. Microsoft also advises disabling content recording in production and not storing secrets, credentials, or tokens in prompts or tool arguments. Reference topics: Microsoft Foundry tracing, LangChain tracing, OpenTelemetry service naming, Application Insights, and secure telemetry configuration.
質問 # 141
You plan to build an app that will generate a list of tags for uploaded images. The app must:
* Generate tags in a user ' s preferred language.
* Support English, French, and Spanish.
* Minimize development effort.
Which Azure service endpoint should you use?
正解：A
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure Vision Image Analysis provides a pretrained image-tagging capability that generates descriptive tags for visual content. The service can identify concepts such as objects, scenes, activities, and other image characteristics and return confidence values for the generated tags.
Because this is a pretrained capability, no custom training dataset, labeling process, training iteration, or prediction-resource deployment is required. That directly satisfies the requirement to minimize development effort.
Image Analysis also supports localized output for supported features and languages, allowing an application to request results appropriate for users who prefer English, French, or Spanish. The application therefore avoids implementing a separate tag-generation model followed by an additional translation workflow.
Azure Custom Vision image classification is intended for business-specific categories and would require labeled training images. Content Safety classifies harmful or inappropriate content rather than producing general-purpose descriptive image tags. Custom Translator can translate text but cannot inspect an image and determine which tags describe it.
The AI-103 Study Guide includes multimodal image analysis and extracting visual characteristics as part of the computer-vision skill domain. Microsoft Learn
質問 # 142
You have a Microsoft Foundry resource named Al1 that hosts three deployments of the GPT 3.5 model. Each deployment is optimized for a unique workload.
You plan to deploy three apps. Each app will access AM by using the REST API and will use the deployment that was optimized for the apps intended workload.
You need to provide each app with access to All and the appropriate deployment. The solution must ensure that only the apps can access AH.
What should you use to provide access to AI1. and what should each app use to connect to its appropriate deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Use Microsoft Entra ID authentication and pass an OAuth 2.0 bearer token in the REST request's Authorization header. Assign the required inference role, such as Cognitive Services User , only to each application's managed identity or service principal. This authenticates the applications as distinct security principals and supports resource-scoped RBAC, auditing, token expiration, and immediate access revocation.
Microsoft recommends Entra ID for production workloads because API keys are static, resource-wide secrets that cannot identify individual callers or provide granular authorization.
Each application must specify its appropriate deployment name when making an inference request. Foundry deployments assign a unique name and configuration to each deployed model. In the deployment-level REST API, that name appears in the request route:
/openai/deployments/{deployment-name}/chat/completions
The resource endpoint and authentication mechanism can remain common across all three applications, while the deployment name directs each request to the GPT-3.5 deployment optimized for that application's workload. A deployment type describes capacity or provisioning characteristics and does not select a particular deployed model.
Study Guide alignment: configure authentication, managed identities, RBAC, model deployments, and REST-based model consumption.
質問 # 143
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
正解：D
解説：
The first step should be an Azure Content Understanding analyzer within Microsoft Foundry Tools.
This specific tool is designed to handle the complexities in this scenario--scanned text, complex tables, and multi-column layouts--by converting unstructured documents into a structured Markdown format. This preserves the document's original hierarchy and structural relationships, which is critical for accurate downstream reasoning by an agent.
Incorrect:
[Not A]
Azure Language is designed for text-based natural language processing (NLP) like sentiment analysis or PII redaction on raw text strings. It cannot parse scanned images, optical character recognition (OCR), tables, or complex multi-column visual layouts.
[Not C]
The first step in this specific Microsoft Foundry agent configuration should not be an Azure OpenAI Responses API call.While multimodal models can read images, they frequently hallucinate data, fail to capture complex multi-column reading orders, and degrade table structures when processing dense, mixed-format documents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/elements
質問 # 144
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ShikenPASSのAI-103試験問題がこの分野で最も人気があるのはなぜですか？ 一方では、すべてのお客様のフィードバックからの統計によると、AI-103ガイドトレントの助けを借りてAI-103試験を準備したMicrosoftお客様の合格率は98％toに達しました。 100％。 一方、シミュレーションテストは、AI-103試験問題のソフトウェアバージョンで利用できます。これは、AI-103試験の雰囲気に慣れるのに役立ちます。 AI-103トレントのDeveloping AI Apps and Agents on Azure質問があなたにとって最良の選択であると信じてください。
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