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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Topic 2: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
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NEW QUESTION # 118
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?
Answer: A
Explanation:
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.
NEW QUESTION # 119
You need to optimize content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Protected material detection feature to run the tests.
Does this meet the requirement?
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Protected material detection addresses a different safety concern from standard objectionable-content moderation. It is intended to identify protected textual material or other supported protected-content classes rather than evaluate normal harm categories such as violence, sexual content, self-harm, and hate/fairness.
To optimize a harmful-content filter, representative prompts should be submitted through the Moderate text content interface. Microsoft documents that the moderation experience returns detected content categories and severity levels and lets the user adjust filter thresholds before rerunning the same content. Microsoft Learn Because the objective is to tune the filter configuration applied to objectionable user input and generated output, the tests must produce the same harm-category signals used by those filters. Protected-material detection cannot provide those severity measurements and therefore cannot determine whether the configured harmful-content thresholds are correct.
Protected-material detection can still be an additional responsible-AI control when an application needs to identify protected or copyrighted-like material, but that is independent of the requirement in this question.
The AI-103 Study Guide distinguishes content moderation, safety filters, risk detection, safety evaluations, and related responsible-AI controls.
NEW QUESTION # 120
You have a Microsoft foundry project that contains a multi-agent solution. The agents use tool calling to query internal systems. You need to implement responsible Al auditing to meet the following requirements:
* Capture all the nested operations across the entire agent run.
* Record tool invocation arguments and returned results as metadata.
What should you use for each requirement? To answer, drag the appropriate options to the correct targets.
Each option 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:
Use hierarchical spans to represent the execution hierarchy of the complete agent run. Distributed tracing models an operation as a span and can create child spans for nested work. In a multi-agent workflow, the top- level agent run can contain nested model calls, sub-agent invocations, retrieval requests, and tool calls, preserving the parent-child relationship among those activities.
This structure allows auditors to reconstruct how a request moved through the agent system rather than seeing only unrelated log records.
Use tool call attributes to store structured metadata about individual tool invocations, including arguments supplied to tools and the results returned. Attributes belong to the relevant tool-call span and allow engineers to inspect exactly what the agent requested and what the external/internal system returned.
Sampling and trace-sampling policies determine which traces are retained; they do not create the operation hierarchy or capture tool metadata. A KQL query filter is used when querying telemetry and is not instrumentation.
The AI-103 Study Guide explicitly lists auditing through trace logging and provenance metadata and observability through tracing, token analytics, safety signals, and latency breakdowns . Microsoft Learn
NEW QUESTION # 121
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses a stored access key to retrieve secrets from an Azure key vault, which violates a keyless-credentials requirement.
You need to ensure that the agent can retrieve the secrets. The solution must follow the principle of least privilege.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Enable a system-assigned managed identity at the project level
The correct configuration is to enable a system-assigned managed identity at the project level.
This is the correct approach. Azure AI Foundry (formerly Azure Machine Learning) allows you to enable a system-assigned managed identity directly at the project level. This identity acts as a keyless credential native to Azure. To follow the principle of least privilege, you then assign this project-level identity a specific role (such as Key Vault Secrets User) restricted only to the required Azure Key Vault. This ensures that the agent can retrieve secrets securely without exposing credentials to other projects or resources Box 2: Assign the Key Vault Secrets User role to the managed identity To comply with the keyless-credentials requirement and follow the principle of least privilege, you should configure the Key Vault authorization method to assign the Key Vault Secrets User role to the managed identity using Azure Role-Based Access Control (Azure RBAC).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/ai-gateway
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 122
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 assess the responses generated by Agent1 when the agent uses the product information stored in storage1. The solution must meet the technical requirements. What should you include in the recommendation?
Answer: B
Explanation:
The best way for the Foundry Agent to access the product information stored in the Azure Blob Storage account is by connecting an Azure AI Search index as an indexed knowledge source via the Agent's file search tool.
This implementation creates a robust Retrieval-Augmented Generation (RAG) framework, ensuring the agent retrieves grounded, high-relevance product information.
Scenario:
Technical Requirements;
*-> Responses generated by using the product sheet information must be relevant, complete, and accurate.
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://learn.microsoft.com/en-nz/answers/questions/5868427/uploading-data-in-microsoft-foundry-agent
NEW QUESTION # 123
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