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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
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NEW QUESTION # 145
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: A
NEW QUESTION # 146
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
The most essential skills for this scenario are Azure OpenAI Embedding and Text Split.
Azure OpenAI Embedding: This skill is critical for generating the vector representations (embeddings) of your text, which directly enables the required vector search capability.
Text Split: This skill is essential because LLMs and embedding models have strict token limits.
Breaking large product detail sheets into smaller chunks ensures the text fits into the embedding model and improves the accuracy of semantic search.
Incorrect:
[Not B]
Entity Recognition: This extracts specific entities like names, dates, or locations. While helpful for advanced filtering, it is not a foundational requirement to enable basic semantic or vector search.
[Not D]
Merge: This skill combines text from multiple fields into a single string. Since product sheets are already unified documents, splitting and chunking them is the priority rather than merging separate fields.
[Not E]
Language Detection: This identifies the language of the input text. Unless your product sheets are completely multilingual and require conditional routing to different language models, this skill is secondary.
[Not F]
Key Phrase Extraction: This pulls out main talking points or keywords. This is primary used for traditional keyword tagging or basic search indexing, whereas your requirement specifically dictates vector and semantic-based retrieval.
Scenario:
Technical Requirements;
*-> The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
Data environment: The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
Planned changes: Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Reference:
https://www.rheininsights.com/blog/en/Retrieval+Augmented+Generation+with+Azure+AI+Search
+and+Atlassian+Confluence.php
NEW QUESTION # 147
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
* Multiple client applications must use the same search configuration.
* A security policy must prevent key-based authentication.
* Administrative effort must be minimized.
What should you do?
Answer: A
Explanation:
The correct solution is to configure an Azure AI Search connection in Project1 and reference that connection from each application. Microsoft Foundry project connections are intended to centralize external resource configuration for a project. The official Foundry guidance states that when adding a connection, you select the external service, such as Azure AI Search , and choose the authentication method for that resource.
This directly satisfies the requirement for multiple client applications to use the same search configuration instead of duplicating endpoints, indexes, and credentials in each app.
The security requirement is met by configuring the connection with keyless authentication. The Azure AI Search integration guidance specifies that a Foundry project connection requires the search endpoint and either key-based authentication or keyless authentication with Microsoft Entra ID . For keyless authentication, RBAC roles are assigned to the project's managed identity, eliminating hard-coded API keys.
Options A and C use secure identity-based access but force every client application to maintain its own Azure AI Search configuration, increasing administration and violating the shared-configuration requirement. Option B also increases administrative work and does not use the native Azure AI Search connection type. Reference topics: Microsoft Foundry project connections, Azure AI Search grounding, Microsoft Entra ID authentication, managed identities, and RBAC.
NEW QUESTION # 148
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.
Answer:
Explanation:
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
NEW QUESTION # 149
You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?
Answer: D
Explanation:
Few-shot prompting is the best approach for this project. This technique improves classification accuracy by including a few high-quality examples directly inside the prompt, giving the model a clear pattern to follow without modifying its weights or storing data permanently.
Zero retraining: Works entirely through in-context learning during the API call.
No permanent storage: The knowledge disappears as soon as the inference request is completed.
Immediate accuracy boost: Demonstrates formatting, nuances, and edge cases directly to the model.
Reference:
https://www.linkedin.com/pulse/top-interview-questions-answers-prompt-engineering-nitin-sharma-ka4fc
NEW QUESTION # 150
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