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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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質問 # 144
You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding locations within the source document.
What should you do?
正解：D
解説：
The correct answer is A. Enable estimateFieldSourceAndConfidence . Azure Content Understanding document analyzers support an opt-in confidence and grounding capability for field extraction. Microsoft documentation states that to opt in for confidence and grounding, you set estimateFieldSourceAndConfidence
= true in the analyzer configuration, or configure estimateSourceAndConfidence = true for specific fields.
This enables each extracted field to include a confidence score and references back to the original document source location.
This directly satisfies both stakeholder requirements: source grounding allows users to verify where the extracted value came from in the scanned PDF, and the confidence score supports downstream automation rules, such as sending low-confidence fields to manual review. Microsoft's analyzer improvement guidance describes confidence scoring as a value between 0 and 1 and grounding as references or citations for extracted outputs to the original document content.
Generative extraction does not guarantee per-field confidence and source grounding. enableSegment is used for document segmentation, not confidence scoring. Labeled samples can improve extraction quality, but they do not by themselves enable confidence and grounding output. Reference topics: Content Understanding document analyzers, field extraction, confidence scoring, grounding, and RAG ingestion.
質問 # 145
You have an Azure App Service app named App1 and provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
正解：D
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Among the supplied choices, the simplest configuration is the endpoint URI and subscription key .
Azure AI/Cognitive Services resources expose a resource endpoint and access keys. An application can call the appropriate service API and place the subscription key in the expected request header without implementing an OAuth token-acquisition flow or managing certificate authentication. This directly minimizes setup and administrative effort.
A SAS token is associated primarily with services such as Azure Storage and is not the standard authentication mechanism for a Foundry/Azure AI services endpoint.
A system-assigned managed identity is a strong production security option, but an X.509 certificate is not required for normal managed-identity authentication. Consequently option B combines unrelated mechanisms.
OAuth/Microsoft Entra authentication is generally preferable when the requirements prioritize keyless access and RBAC, but it requires identity and role configuration. The question specifically prioritizes minimal administrative effort , and option D represents the direct key-based access mechanism.
For production architectures where secret elimination is required, Microsoft recommends Microsoft Entra ID and managed identities when supported. The AI-103 Study Guide covers both managed identity and other authentication approaches under system security. Microsoft Learn
質問 # 146
You are building an app that will include one million scanned magazine articles. Each article will be stored as an image file. You need to configure the app to extract text from the images. The solution must minimize development effort.
正解：B
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The Read API is the Azure Vision capability specifically optimized for optical character recognition of text- heavy images and documents. The application can submit each scanned magazine page and receive recognized words and lines without developing or training a custom OCR model.
This minimizes development effort because Microsoft ' s pretrained OCR models handle printed text directly.
The Read operation is designed for scenarios such as scanned documents, signs, forms, and other images in which extracting textual content is the principal objective. For large-scale workloads, applications use the asynchronous Read workflow: submit the image, obtain an operation identifier, poll for completion, and then process the resulting text.
Azure Vision Image Analysis provides broader image-understanding functionality such as tags, captions, objects, and other visual features, but the Read capability is the specialized choice for document-style OCR.
Document Intelligence is preferable when the requirement extends to document structure, tables, fields, key- value pairs, or prebuilt document schemas. Azure Language operates on text after extraction and therefore cannot perform OCR directly.
The AI-103 Study Guide includes RAG ingestion using optical character recognition (OCR) and multimodal content extraction as tested skills.
質問 # 147
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
正解：D
解説：
You should implement an evaluation and retry loop in your application code.
You must wrap the agent call inside a conditional code loop that evaluates the output against required criteria (such as a checklist of regulatory clauses), and programmatically forces a retry if information is omitted.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
質問 # 148
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever.
You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
正解：B
解説：
The correct indexing approach is to use an indexer to extract image data into a normalized_images collection .
In Azure AI Search enrichment pipelines, embedded images in PDFs are not passed directly from the text content field into OCR. Instead, the indexer must perform document cracking and image extraction by enabling the indexer image action. Microsoft's Azure AI Search documentation states that image-processing skills such as OCR and image analysis expect normalized images, and that enabling imageAction causes embedded images to be extracted and normalized for downstream skills.
The OCR skill is designed to receive image input from /document/normalized_images/*. Microsoft's skillset tutorial specifically states that the OCR skill assumes a normalized_images field exists and that this field is generated by setting the indexer imageAction configuration to generateNormalizedImages. The document extraction skill reference also confirms that generateNormalizedImages creates an array of normalized images during document cracking for OCR and image analysis.
Option A is incorrect because OCR does not run directly against the index content field. Option B maps outputs after enrichment; it does not extract images. Option D reshapes data but does not create the required normalized image collection. Reference topics: Azure AI Search indexers, AI enrichment, OCR skill, imageAction, and normalized_images.
質問 # 149
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