私たちのAI-103練習問題は実際に自分の魅力を持っているため、世界中のユーザーを引き付けました。AI-103練習問題のように、あらゆる面でユーザーのニーズを真剣に検討する練習問題がないです。AI-103練習問題を利用すれば、AI-103試験に合格することは夢ではないです。従って、ためらわなくて、AI-103練習問題を購入し、勉強し始めましょう！
| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
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質問 # 133
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：C
質問 # 134
You have a product support manual.
You need to build a product support chatbot based on the manual. The solution must minimize development effort and costs.
What should you use?
正解：C
解説：
Azure OpenAI On Your Data makes it easier for developers to connect, ingest and ground their enterprise data to create personalized copilots (preview) rapidly. It enhances user comprehension, expedites task completion, improves operational efficiency, and aids decision- making.
Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
質問 # 135
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
正解：C
解説：
The correct capability is token usage . In Microsoft Foundry observability, token consumption is the primary signal for diagnosing model-cost changes when request volume is unchanged. Token usage lets you distinguish whether costs increased because prompts became larger, retrieved or tool-provided context expanded, responses became longer, or agent execution added more model calls. Microsoft Foundry monitoring dashboards track operational metrics such as token consumption, latency, error rates, and quality scores, and the agent monitoring dashboard is specifically intended to help analyze token usage, latency, success rates, and evaluation outcomes for production traffic.
This directly matches the scenario because the issue is not more traffic, but changed request or response characteristics. Input tokens reveal whether the prompt, chat history, grounding data, or tool outputs being sent to the model increased. Output tokens reveal whether the model is generating longer completions.
Expanded tool usage can also increase cost indirectly by adding more tool results, intermediate calls, and context into subsequent model requests; Foundry tracing and observability capture tool usage and token consumption for agent runs.
Evaluation metrics assess response quality and safety, not cost drivers. Latency identifies performance delays, and run success rate measures reliability. Reference topics: Microsoft Foundry observability, agent monitoring dashboard, token consumption, cost analysis, tool usage, and production monitoring.
質問 # 136
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
正解：C
解説：
To remedy the "invalid project ID" error when calling the Azure Speech to Text REST API within your Microsoft AI Foundry project, you must update the project property in your API request body from a plain string name/ID to the fully qualified Azure Resource URI of the project.
The Speech to Text REST API (v3.0 and later) strictly expects resource links formatted as URIs rather than individual ID strings.
*-> Step 1: Construct the Correct Project URI
You need to pass the project as an absolute object reference. Construct your project property value using the following format:text
https://<your-region>://<your-project-guid>
Use code with caution.Replace <your-region> with your actual Azure Speech resource region (e.g., eastus, westeurope).Replace <your-project-guid> with the actual system-generated Unique Identifier of your project.
Step 2: Retrieve your Project GUID
Step 3: Update your Agent / REST API Request Body
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/azure-ai-speech
質問 # 137
You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?
正解：C
解説：
The Azure AI Content Safety image model uses a trimmed severity scale that returns only 0, 2, 4, and 6, so an odd value such as 3 cannot occur for image content. The text model uses the full 0 to 7 scale, which is where odd-numbered severities appear.
質問 # 138
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AI-103参考書内容: https://jp.fast2test.com/AI-103-premium-file.html