AI-103認証pdf資料 & AI-103日本語受験教科書

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AI-103認証pdf資料, AI-103日本語受験教科書, AI-103オンライン試験, AI-103関連試験, AI-103認定試験

Microsoft　AI-103資格認定はIT技術領域に従事する人に必要があります。我々社のMicrosoft　AI-103試験練習問題はあなたに試験うま合格できるのを支援します。あなたの取得したMicrosoft　AI-103資格認定は、仕事中に核心技術知識を同僚に認可されるし、あなたの技術信頼度を増強できます。

Microsoft AI-103 Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Plan and manage Azure AI solutions 25–30% - Design Azure AI infrastructure
  • 1. Select appropriate Azure AI Foundry services
  • 2. Design for scalability, availability, and cost optimization
  • 3. Plan for security, compliance, and responsible AI
- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Integrate with CI/CD pipelines
  • 3. Configure model and agent deployments
Topic 2: Implement computer vision solutions 10–15% - Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content
- Implement image analysis and processing
  • 1. Extract text and structure from images
  • 2. Implement object detection and image classification
  • 3. Use Azure AI Vision services
Topic 3: Implement information extraction and knowledge mining 10–15% - Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content
- Build knowledge bases and search solutions
  • 1. Design knowledge mining pipelines
  • 2. Create and manage vector indexes
  • 3. Implement Azure AI Search
Topic 4: Implement generative AI and agentic solutions 30–35% - Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Implement multi-agent workflows and orchestration
  • 3. Select agent architecture patterns
  • 4. Manage state, memory, and context
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Integrate Azure OpenAI and other models
  • 3. Implement function calling and tool use
  • 4. Build retrieval-augmented generation (RAG) solutions
Topic 5: Implement text and speech analysis solutions 10–15% - Implement natural language processing
  • 1. Build conversational language understanding
  • 2. Use Azure AI Language services
  • 3. Perform sentiment analysis, entity recognition, and summarization
- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration

>> AI-103認証pdf資料 <<

AI-103日本語受験教科書 & AI-103オンライン試験

MicrosoftのAI-103のオンラインサービスのスタディガイドを買いたかったら、PassTestを買うのを薦めています。PassTestは同じ作用がある多くのサイトでリーダーとしているサイトで、最も良い品質と最新のトレーニング資料を提供しています。弊社が提供したすべての勉強資料と他のトレーニング資料はコスト効率の良い製品で、サイトが一年間の無料更新サービスを提供します。ですから、弊社のトレーニング製品はあなたが試験に合格することを助けにならなかったら、全額で返金することを保証します。

Microsoft Developing AI Apps and Agents on Azure 認定 AI-103 試験問題 (Q153-Q158):

質問 # 153
You have a Microsoft Foundry project that contains a Retrieval Augmented Generation (RAG) chat solution used by customer support agents.
You are adding an automated pre-production evaluation step to a CI/CD pipeline named Pipeline1. The evaluation will run against a labeled test dataset that contains support questions and the expected grounding context.
You need to ensure that Pipeline1 fails if unsupported content or a retrieval mismatch exceeds a defined threshold:
- responses include claims not supported by the retrieved source
content
- retrieved source content does not align with the labeled expected
context
Which two built-in evaluators should you use in Pipeline1? Each correct answer presents pat of the solution.
NOTE: Each correct selection is worth one point.

  • A. Coherence
  • B. Groundedness
  • C. Fluency
  • D. Retrieval
  • E. Response Completeness

正解：B、D

解説：
The Groundedness Evaluator evaluator validates that the model's responses include only claims supported by the retrieved source content It flags ungrounded content or hallucinations. If the average score drops below your defined threshold, it triggers a pipeline failure.
The correct additional built-in evaluator appropriate for the pipeline is Retrieval (specifically, the RetrievalEvaluator or DocumentRetrievalEvaluator).
A standard RAG evaluation pipeline assesses both the generator (the LLM producing the answer) and the retriever (the search system pulling documentation). The CI/CD requirements specify two distinct failure thresholds:
Responses including claims not supported by the retrieved source content: This checks for model hallucinations and is handled by the Groundedness Evaluator.
Retrieved source content not aligning with the labeled expected context: This explicitly measures the performance of your search step against your ground-truth data. The built-in Retrieval evaluator maps to this requirement. It computes metrics like context recall to ensure your system successfully retrieves the exact reference documents specified in your labeled test dataset Reference:
https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/evaluations-cli-evaluators


質問 # 154
In Microsoft Foundry, you use the Chat playground with the GPT-35 Turbo model. You have a prompt that contains the following code.

You need the model to create an explanation of the code. The solution must minimize costs. What should you do?

  • A. Set the temperature parameter to 1.
  • B. Change the model to GPT-4-32k.
  • C. Add function F (explanation) to the prompt.
  • D. Add// what does function F do? to the prompt.

正解：D

解説：
Add the natural-language comment // what does function F do? after the code. The comment gives GPT-35 Turbo a direct and unambiguous instruction to analyze and explain function F. Because it uses syntax that naturally belongs beside source code, the model can distinguish the program being analyzed from the requested task and generate an explanatory completion. Microsoft's prompt-engineering guidance recommends clearly stating the required outcome and using cues that direct the model toward the desired response.
This approach retains the existing GPT-35 Turbo deployment and adds only a small number of prompt tokens, satisfying the cost-minimization requirement. Changing to GPT-4-32k would be unnecessary because the requirement is a straightforward code-explanation task and does not indicate that a substantially larger context window is required.
Adding function F(explanation) resembles a new function declaration or invocation rather than an instruction to explain existing code. The model could interpret it as source code that must be completed. Setting temperature to 1 changes output randomness but does not tell the model to explain the function; it can also make the answer less consistent.
Study Guide alignment: Use prompt-engineering techniques, construct clear model instructions, tune generation behavior, and select an appropriate model based on quality and cost requirements.


質問 # 155
You have a Microsoft Foundry project that contains an agent named Agent1.
Agent runs successful, but Foundry Control Plane does NOT display values for error rates, runs, and token usage, and the Traces tab is empty.
You need to ensure that Found Control Plane displays the appropriate values for Agent1.
What should you do?

  • A. Update Agent1 to a new version.
  • B. Assign to a Log Analytics workspace to Agent1.
  • C. Enable Application Insights for Agent1.
  • D. Restart Agent from Foundry Control Plan

正解：C

解説：
To resolve this issue, you must connect and configure an Azure Application Insights resource for your Microsoft Foundry project.
The Foundry Control Plane, its Agent Monitoring Dashboard, and the Traces tab rely directly on telemetry data stored within the connected Application Insights instance. If this resource is missing, unlinked, or improperly configured, the dashboard cannot display runs, error rates, token usage, or transaction spans.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/monitoring--observability-in-microsoft-foundry/4517250


質問 # 156
Drag and Drop Question
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:
Box 1: string
The Type (string): While you want the output schema to contain a description of the color palette or style, Azure Content Understanding schemas map the underlying data types using fundamental types like string, number, or array. The field will output the final result in the JSON response under a key like "valueString" Box 2: generate The Method (generate): The extract method is only used for pulling literal text exactly as it appears in content (such as speech-to-text transcripts or OCR). Because analyzing visual aesthetics and synthesizing descriptive text about a "color scheme" requires multimodal AI reasoning, you must use the generate method. This instructs the underlying model to analyze the segment and synthesize a completely new descriptive insight.
References:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/video/elements


質問 # 157
Hotspot Question
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：


質問 # 158
......

AI-103「Developing AI Apps and Agents on Azure」試験は簡単ではありません。専門的な知識が必要で、もしあなたはまだこの方面の知識を欠かれば、PassTestは君に向ける知識を提供いたします。PassTestの専門家チームは彼らの知識や経験を利用してあなたの知識を広めることを助けています。そしてあなたにAI-103試験に関するテスト問題と解答が分析して差し上げるうちにあなたのIT専門知識を固めています。

AI-103日本語受験教科書: https://www.passtest.jp/Microsoft/AI-103-shiken.html

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