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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Topic 2: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Topic 3: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 4: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 5: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
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NEW QUESTION # 56
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used
once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
" tool_choice " : " required "
The correct completion is " tool_choice " : " required " . In Microsoft Foundry Agent Service, tool_choice controls whether the model can answer directly or must invoke a tool during a run. The official Foundry tool guidance states that tool_choice provides deterministic control over tool calling: auto allows the model to decide whether to call tools, none prevents tool use, and required forces the model to call one or more tools.
This directly addresses the issue where the ticket-triage agent sometimes responds without invoking a required tool.
The completed payload should therefore add the tool_choice property beside assistant_id, with the value " required " . The value " auto " is incorrect because it preserves the current nondeterministic behavior. The values " tools " and " type " do not force execution-time tool invocation in this payload; they are used for tool definitions or typed objects in other contexts. response_format controls output formatting, not tool execution.
Reference topics: Microsoft Foundry Agent Service, tool calling reliability, run payload configuration, tool_choice, agent execution, and deterministic tool invocation.
NEW QUESTION # 57
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 # 58
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl 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: D
Explanation:
The correct answer is A. Implement a retry policy that uses exponential backoff and jitter . HTTP 429 indicates that the request rate or token rate has exceeded the configured service limits for the model deployment. Microsoft Foundry Agent Service limits guidance specifically recommends implementing exponential backoff with jitter in application retry logic when agents receive rate-limit 429 errors. It also recommends reviewing Azure OpenAI quotas and token-per-minute and request-per-minute limits for the deployment.
This approach improves reliability while remaining within service limits because retries are delayed progressively instead of immediately adding more pressure to an already throttled deployment. Microsoft Foundry Models quota guidance also states that unsuccessful requests still count toward per-minute rate limits and that continuously resending requests without backing off makes throttling worse. It recommends retry logic with exponential backoff and using the Retry-After header when available.
Creating a new thread and retrying immediately does not change the deployment's rate limits and can worsen throttling. Reducing registered tools may simplify orchestration but does not directly address model RPM or TPM limits. Splitting uploaded content into smaller files may help ingestion scenarios, but it is not the correct throttling control for intermittent HTTP 429 model calls. Reference topics: Foundry Agent Service limits, Azure OpenAI quota management, throttling, retry policies, and production reliability.
NEW QUESTION # 59
You have a custom named entity recognition (NER) project in Azure Language in Foundry Tools for support tickets. The schema for the project contains an entity type named ContactInfo.
In tagged training files, ContactInfo is used for phone numbers, email addresses, and social media handles.
Model evaluation shows low precision for ContactInfo, including false positives in which nearby text is extracted as ContactInfo.
You need to improve the precision of the project.
What should you do before retraining the model?
Answer: A
Explanation:
To improve the precision of the project before retraining the model, you should replace ContactInfo by using Phone, Email, and SocialMedia entities, and then relabel every matching span.
Reduces Model Confusion: Combining distinct types of data (like phone numbers, emails, and usernames) into a single generic entity (ContactInfo) creates conflicting patterns for the model.
Splitting them allows the model to learn the specific, unique syntax rules of each type.
Eliminates Over-extraction: When an entity is too broad, the model struggles to identify where the entity begins and ends, leading to false positives by grabbing adjacent text. Distinct entities provide sharper, cleaner boundaries.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/overview
NEW QUESTION # 60
You are defining an agent in Microsoft Foundry Agent Service. The agent uses a catalogue model for reasoning, a system instruction that sets its goals, and a file search capability that reads a knowledge store. Which three components does this combination represent?
Answer: B
Explanation:
Microsoft Foundry Agent Service defines every agent as the combination of three core components: a model that provides reasoning, instructions that set goals and behaviour, and tools that give access to data or actions. The scenario maps exactly, with the catalogue model, the system instruction, and the file search tool.
NEW QUESTION # 61
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