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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 2: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 3: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
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質問 # 17
You have an Azure subscription that contains an Azure AI Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure AI Content Understanding project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?
正解：D
解説：
Cognitive Services Contributor
This role is typically granted access at the resource group level for a user in conjunction with additional roles. By itself this role would allow a user to perform the following tasks.
* Create new Azure OpenAI resources within the assigned resource group.
* Etc.
To create an Azure AI Content Understanding project on an Azure AI Foundry hub, you need either the Azure AI Account Owner, Contributor, or Cognitive Services Contributor role at the subscription level to create the necessary Azure AI Foundry resource, and then the user must have the Azure AI User role at the project level to create the agent within that project.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control
質問 # 18
Hotspot Question
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
- Use a single key and endpoint to access multiple services.
- Consolidate billing for future services that you might use.
- Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: PUT
Need to create a Cognitive Services Multi-Service resource using the PUT method. This specific resource type provides a single endpoint and key for multiple AI services, consolidates billing, and supports Azure Vision.
Box 2: CognitiveServices
Using the CognitiveServices kind creates a multi-service resource. This fulfills all the requirements by providing a single key and endpoint for multiple services, consolidating billing, and enabling access to features like Azure AI Vision and Text Analytics under one roof.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview
質問 # 19
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.
正解：A、B
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 # 20
You need a model that generates vector representations of internal policy-document text and user queries.
正解：A
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use an embedding model . Embedding models convert text into numerical vector representations whose geometric relationships encode semantic similarity.
During indexing, policy documents are normally divided into chunks. Each chunk is submitted to the embedding model and the resulting vector is stored in a vector field in Azure AI Search or another vector store.
At query time, the user ' s natural-language question is submitted to the same embedding model. The resulting query vector can then be compared with the stored document vectors by using a nearest-neighbor similarity algorithm. Semantically similar passages can therefore be retrieved even when the query and policy use different wording.
A large language model or small language model can subsequently use the retrieved text to generate an answer, but the LLM/SLM is not the component specifically intended to create search vectors. An image- generation model creates visual content and is unrelated to textual vector retrieval.
Embedding models are therefore foundational to vector search and many RAG ingestion and retrieval architectures.
Study Guide alignment: configure semantic, hybrid, and vector search for grounding and build retrieval pipelines for internal documents . Microsoft Learn
質問 # 21
You have an invoice-processing application named App1 that uses Azure Content 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 invoice against static vendor contract 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：D
解説：
Configure a multi-file task in pro mode . At analysis time, provide the invoice and its related purchase order as the input documents. During analyzer creation, add the static vendor contracts as reference data . Pro mode can reason across multiple input documents and use reference documents as contextual knowledge for validation, enrichment, and discrepancy detection. Microsoft specifically documents the scenario of supplying an invoice and purchase order as inputs while using contract files as reference data to identify inconsistencies.
Define a field schema containing the required invoice information and generated discrepancy findings.
Building the task creates an analyzer ID that applications invoke through one analyzer API endpoint, producing a unified structured result governed by that schema.
Standard mode is optimized for straightforward processing of individual files. It does not support cross-file analysis, reference-dataset integration, or the multi-step reasoning required to compare invoices, purchase orders, and contractual conditions. A "multi-file task in standard mode" is therefore not a supported configuration. Confidence scores do not address document comparison and, notably, are not available in pro mode.
Study Guide alignment: configure Content Understanding analyzers, design structured schemas, process multiple documents, integrate reference data, and implement document validation workflows .
質問 # 22
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