AI-103はMicrosoftのひとつの認証で、AI-103がMicrosoftに入るの第一歩として、AI-103「Developing AI Apps and Agents on Azure」試験がますます人気があがって、AI-103に参加するかたもだんだん多くなって、しかしAI-103認証試験に合格することが非常に難しいで、君はAI-103に関する試験科目の問題集を購入したいですか？
| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
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質問 # 53
You are creating an enrichment pipeline that will use Azure Al Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Use an object projection for the unstructured JSON data. Object projections store a JSON representation of an enrichment tree node in an Azure Blob Storage container. They preserve hierarchical fields and complex structures, making them appropriate when enriched document content must remain available as a complete JSON object rather than being decomposed into relational rows. Microsoft describes object projections as JSON representations that can be sourced from nodes in the enrichment tree.
For the scanned PDF content, use a file projection . During document cracking and OCR processing, scanned pages are represented through the /document/normalized_images/* collection. File projections write these binary normalized images to Blob Storage, preserving the page assets from which the text is extracted.
Microsoft specifies that file projections operate only on normalized images and contain binary data rather than JSON.
A table projection is intended for row-and-column structures used by analytical tools such as Power BI. It is not the appropriate choice for preserving hierarchical JSON or scanned-document image files.
Study Guide alignment: configure Azure AI Search enrichment pipelines, skillsets, knowledge stores, and table, object, and file projections .
質問 # 54
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
正解：B
解説：
To enable the agent to answer questions about order status, you need a plugin.
In Semantic Kernel, a plugin acts as a container for the specific capabilities (functions) that allow an agent to interact with external data or systems, such as an order database or API.
To help me give you a more specific example or guide, could you tell me:
The data source where order info is kept (e.g., a SQL database, a REST API, or Shopify) The programming language you're using for the Kernel (C# or Python) Reference:
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions
質問 # 55
You need Pipeline1 to fail if:
* responses include claims not supported by retrieved source content;
* retrieved source content does not align with the labeled expected context.
Which two built-in evaluators should you use?
正解：A、D
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Groundedness to detect claims in the generated answer that are not supported by the retrieved source context. Groundedness is fundamental to RAG evaluation because it determines whether the model stays faithful to the evidence supplied to it rather than introducing unsupported information.
Use the Retrieval evaluator to measure whether the search/retrieval stage returns content that aligns with the expected grounding information in the labeled dataset. A failure at this stage means the generation model might receive incomplete or irrelevant evidence even if it behaves correctly.
These two evaluators therefore inspect distinct parts of the RAG pipeline:
* Retrieval evaluates the evidence selected by the retrieval system.
* Groundedness evaluates whether the generated answer is supported by that evidence.
Response Completeness evaluates whether an answer sufficiently covers expected information. Fluency measures linguistic quality, and Coherence assesses organization and logical readability. None directly covers the two specified failure modes.
This combination is appropriate for a CI/CD quality gate because retrieval regressions and unsupported generation can be detected separately before production deployment.
The AI-103 Study Guide explicitly covers evaluating models and apps for fabrications, relevance, quality, and safety , together with monitoring grounding quality. Microsoft Learn
質問 # 56
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：B
解説：
To identify whether model input, model output, or expanded tool usage is driving the cost spike, you should focus on token usage.
Granular tracking of token usage breaks down the costs directly:
Input vs. Output Tokens: Tracing token usage isolates whether the user prompt (input) has grown or if the model is generating longer, more expensive responses (output).
Tool Calling: Expanded tool usage consumes extra tokens each time the agent schema processes an execution loop, which will clearly reflect as inflated token counts.
Reference:
https://www.mindstudio.ai/blog/subtraction-principle-agent-harness-optimization
質問 # 57
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：
Explanation:
type: ask_question
condition: approval == " approved "
The approval step must use type: ask_question because the workflow must pause and wait for a human response before the refund execution proceeds. Microsoft Foundry workflows support human-in-the-loop patterns where the workflow asks the user a question and awaits input before continuing; this pattern is explicitly intended for approval requests and clarifying questions. The workflow guidance also identifies workflows as declarative sequences that orchestrate agents and business logic, including branching logic and human-in-the-loop steps.
The refund execution step must use condition: approval == " approved " so that the second invocation of PaymentAgent runs only when the approval response matches the required approval value. Using true would always execute the refund, bypassing the approval control. Using propose_refund.output != null would only confirm that the first agent step produced output; it would not prove that a human approved the refund.
data_transformation is also incorrect for the approval node because it sets or parses values rather than pausing for user input. Reference topics: Microsoft Foundry workflows, human-in-the-loop workflow pattern, YAML workflow editing, agent orchestration, conditional execution, and workflow approval gates.
質問 # 58
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