With years of experience in the field, VCE4Dumps are always striving hard to provide customers with genuine Developing AI Apps and Agents on Azure (AI-103) exam dumps so that they crack their Developing AI Apps and Agents on Azure (AI-103) exam in less time. VCE4Dumps also offer the best self-assessment software so besides memorizing AI-103 Exam Questions, applicants put their learning to the test and reduce their chances of failure in the real Developing AI Apps and Agents on Azure (AI-103) examination.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 2: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 3: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 4: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
>> Technical AI-103 Training <<
In today's society, there are increasingly thousands of people put a priority to acquire certificates to enhance their abilities. With a total new perspective, our AI-103 study materials have been designed to serve most of the office workers who aim at getting a AI-103 certification. Our AI-103 Test Guide keep pace with contemporary talent development and makes every learner fit in the needs of the society. There is no doubt that our AI-103 latest question can be your first choice for your relevant knowledge accumulation and ability enhancement.
NEW QUESTION # 64
You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?
Answer: D
Explanation:
To meet your requirements, you need to configure a Hybrid Search with Semantic Ranking in Azure AI Search. This setup combines keyword matching for exact identifiers with vector search for natural language queries, delivering the most accurate grounding data to your Microsoft Foundry agent.
Reference:
https://www.tredence.com/blog/searchsmart-enhancing-rag-with-azure-ai-search-service
NEW QUESTION # 65
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: C
Explanation:
The correct answer is A. an Azure Content Understanding in Foundry Tools analyzer . In Azure Content Understanding, an analyzer is the reusable configuration that defines what content type is processed, which elements are extracted, and how the output is structured. Microsoft's analyzer guidance states that analyzers define extraction for text, layout, tables, fields, and output formats such as Markdown and JSON. This makes the analyzer the first required configuration step before downstream reasoning or agent orchestration can use the extracted content.
This matches the requirement because the documents contain scanned text, tables, and multicolumn layouts.
Content Understanding document analysis is designed to transform unstructured documents into structured, machine-readable output while preserving document structures and relationships. Its Markdown representation converts unstructured documents into GitHub Flavored Markdown while maintaining content and layout for downstream use.
A generative chat completion request or Azure OpenAI Responses API call could reason over extracted content, but it is not the correct first step for OCR, layout preservation, and Markdown conversion. Azure Language focuses on text analysis after content has already been extracted. Reference topics: Content Understanding analyzers, document extraction, OCR, layout analysis, Markdown output, and downstream reasoning.
NEW QUESTION # 66
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 personalized user interactions for Agent1. The solution must meet the business requirements.
What should you include in the solution?
Answer: A
Explanation:
Scenario, 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.
To configure personalized user interactions that retain context and recall previous information, you should use the Memory in Foundry Agent Service.
Managed Long-Term Memory: This allows the agent to extract meaningful information from user conversations and store it as durable knowledge (e.g., user preferences or historical details) to be recalled across future interactions.
Scoped Memory Stores: By using the scope parameter when creating memory stores, you can segment memories to ensure a secure, personalized experience for distinct users.
Automatic Injection: The agent will automatically inject these learned facts and user profile details into the prompt before new interactions, eliminating the need to rehydrate context manually.
References:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/what-is-memory
NEW QUESTION # 67
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk
NEW QUESTION # 68
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 # 69
......
Thousands of AI-103 certification holders provide helpful input to VCE4Dumps. It helps us to keep our AI-103 exam dumps preparation material polished, updated, and error-free. To achieve its mission, VCE4Dumps offers a free demo of the Microsoft AI-103 exam questions.This free trial enables customers to evaluate the quality of the Microsoft AI-103 Dumps before making a purchase. You will also receive up to 1 year of free Microsoft AI-103 exam question updates. VCE4Dumps guarantees that nothing will prevent you from clearing the AI-103 exam on your first attempt if you diligently study from our updated AI-103 exam questions.
Real AI-103 Exam Questions: https://www.vce4dumps.com/AI-103-valid-torrent.html