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| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 2: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 3: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 4: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 5: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
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NEW QUESTION # 108
You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?
Answer: A
Explanation:
The Protected material text detection feature in Azure AI Content Safety is specifically designed to identify and block responses that may contain copyrighted or protected content, such as lyrics from popular songs. Since GPT-4 may have been trained on publicly available data, this feature helps ensure that the chatbot does not generate copyrighted lyrics by detecting and filtering out protected material.
NEW QUESTION # 109
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
Answer: A
Explanation:
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
NEW QUESTION # 110
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 recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1. What should you include in the recommendation?
Answer: B
Explanation:
The best choice to include to support this scenario is Azure AI Search.
Retrieval-Augmented Generation (RAG): Azure AI Search acts as the primary knowledge base and indexing infrastructure for AI agents. It ingests raw unstructured data directly from Azure Blob Storage, indexes it, and creates vector embeddings.
Natural Language Queries: It supports semantic ranking and vector search. This allows the Foundry agent to handle user questions written in natural language and find exact matches or conceptual relevance.
Relevance and Accuracy: By leveraging grounding data through an indexer, it ensures the agent's responses are strictly tied back to the product detail sheets, preventing hallucinations and verifying accuracy.
Incorrect:
[Not B]
Grounding with Bing Search: This tool connects the agent to the live internet. Because the product sheets are proprietary company data stored securely inside your private Azure Blob Storage account, Bing Search cannot access them and will not help answer specific questions about Contoso products.
[Not D]
Why Document Intelligence is Incorrect
Extraction vs. Retrieval: Azure Document Intelligence is used to extract structure, text, tables, and form fields from files to turn unstructured data into structured data formats.
Missing Search Infrastructure: It does not possess a native search indexer, vector database, or semantic query engine to accept natural language user questions and dynamically return grounded answers. It is instead used as a tool prior to or alongside Azure AI Search to help clean and structure highly complex layouts during the data ingestion pipeline.
Scenario:
Technical Requirements;
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.
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://pub.towardsai.net/build-a-rag-powered-ai-agent-with-microsoft-foundry-and-foundry-iq-via-the-azure-portal-7eb138b4ae8f
NEW QUESTION # 111
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 112
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
Answer: A
Explanation:
To handle HTTP 429 throttling and improve load testing reliability for your Microsoft Foundry Agent Service, implement exponential backoff with jitter on the client side, deploy a load- balancing gateway across multiple Azure OpenAI regions, or upgrade your deployment to Provisioned Throughput Units (PTU).
*-> 1. Implement Client-Side Retry LogicConfigure your agent's HTTP client or SDK to handle
429 errors gracefully rather than failing immediately.
Exponential Backoff: Increase the wait time between subsequent retry attempts exponentially .
Jitter: Add a small amount of random delay (jitter) to the backoff time to prevent a "thundering herd" effect where all throttled requests retry simultaneously.
Header Inspection: Programmatically read the Retry-After or x-ratelimit-reset values from the HTTP 429 response headers to pause execution for the exact duration requested by Azure.
2. Set Up Multi-Region Load Balancing
3. Switch to Provisioned Throughput (PTU)
4. Optimize Token Consumption
Reference:
https://learn.microsoft.com/en-us/answers/questions/1518859/help-with-resolving-ptu-m-service-429-error
NEW QUESTION # 113
......
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