AI-103学習ガイドは、世界で非常に効率的なツールです。私たちに知られているように、私たちの現代世界では、誰もがより速く、より良く、よりスマートに物事を行うことを求めているので、生産性ハックが信じられないほど人気があるのも不思議ではありません。そのため、学習ツールの重要性を認識する必要があります。お客様の学習効率を高めるために、当社のAI-103トレーニング資料は、当社の多くの専門家によって設計されました。 AI-103学習教材は、すべての人々が学習効率を向上させるのに非常に役立ちます。
| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
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質問 # 110
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of all the files that can be uploaded to the service?
正解：C
解説：
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
質問 # 111
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You add a reflection pass that regenerates the response if the required clauses are missing.
Does this meet the goal?
正解：A
解説：
Correct:
* You add a reflection pass that regenerates the response if the required clauses are missing.
This is Self-Correction Strategy: A reflection pass allows an agent to evaluate its own initial output against specified constraints (e.g., checking for the presence of mandatory regulatory clauses). If the required text is missing, the agent triggers a programmatic self-correction or regeneration loop to include them before final delivery.
Incorrect:
* You increase the value of the max_tokens parameter.
Increasing the max_tokens parameter prevents the response from being cut off mid-sentence due to length constraints. However, it does not force the model's logic to explicitly include missing information that it chose to leave out earlier in the text.
* You increase the value of the temperature parameter.
Raising the temperature parameter increases randomness and creativity. For rigid compliance tasks like summarizing regulatory documents, higher temperature actually increases the risk of hallucination and omission.
* You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Evaluation Flow Block: Running an evaluation flow to score completeness and blocking bad responses identifies and stops low-quality outputs, but it does not fix or actively improve the response completeness. It simply filters failures out of the system.
Reference:
https://pub.towardsai.net/reflection-with-llm-how-to-make-ai-review-its-own-work-2db122fca1d8
質問 # 112
You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers.
The results are often
incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?
正解：C
解説：
The correct answer is Azure Content Understanding in Foundry Tools . The scenario requires more than basic OCR because scanned invoices have varied layouts and multipage tables. Content Understanding is designed for intelligent document processing and provides OCR, layout detection, table extraction, field extraction, confidence scores, and grounding in a managed service. Microsoft describes Content Understanding as a service that transforms unstructured content into structured outputs and supports invoice processing by extracting and validating fields from complex documents.
This also meets the requirement to avoid training a custom model. Content Understanding includes prebuilt and domain-specific analyzers, including invoice and procurement-style document processing, and Microsoft states that these analyzers provide structured extraction without custom training. It generalizes across visual template variations by using semantic document categories rather than requiring separate models per invoice layout.
Azure Machine Learning would increase administrative effort because it requires model development, training, deployment, and monitoring. Azure Language is optimized for text analytics tasks such as classification and entity extraction after text is available, but it does not provide document layout analysis or multipage table structure extraction. Reference topics: Content Understanding, intelligent document processing, OCR, layout analysis, analyzers, field schemas, and structured extraction.
質問 # 113
You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?
正解：D
解説：
You should set the input_fidelity parameter to "high" in this scenario.
In Microsoft Foundry (built on Azure AI Foundry), the input_fidelity parameter specifically dictates how strictly the image generation model adheres to the visual features, layout, and style of the source image.
Why "high" is the Correct Choice
Preserves Product Identity: Setting input_fidelity="high" ensures that critical branding details, logos, structures, and product contours are preserved exactly as they appear in the original photo.
Designed for E-commerce: Microsoft explicitly designed this setting for e-commerce, commercial marketing assets, and product photography to prevent the model from hallucinating or altering key product characteristics.
Model Support: This parameter is natively supported when configuring agent image generation tools (such as the ImageGenerationTool or ImageGenTool classes in the Foundry Agent Service) leveraging advanced models like GPT-image-1 or GPT-image-2.
Reference:
https://developers.openai.com/cookbook/examples/generate_images_with_high_input_fidelity
質問 # 114
You have an invoice-processing application named App1 that uses Azure Constant Understanding in Foundry Tools.
You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
- Compare an invoice to its related purchase order
- Validate the voice against static vendor contact documents
- Return a single structured output that includes discrepancy findings
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?
正解：A
解説：
Multiple-file task: Required over a single-file task. Your pipeline needs to reconcile data across two separate active transactional documents (the invoice and the purchase order) within a single analyzer request.
Pro mode is required instead of standard mode. Pro mode is specifically designed for advanced scenarios requiring multi-step reasoning, cross-file analysis, and validation against a knowledge base. Note that Pro mode currently supports classify and generate fields but does not support confidence scores for specific extracted fields.
Vendor contract files as reference data: Required. Because the vendor contracts are static compliance documents, they should be uploaded and treated as the analyzer's background knowledge base (reference data) to guide the validation logic.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/standard-pro-modes
質問 # 115
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