BTW, DOWNLOAD part of PrepAwayPDF AI-103 dumps from Cloud Storage: https://drive.google.com/open?id=1ZRyRKb2WcOd8Sobq12Xy38yOjDt0Bykz
A lot of effort, commitment, and in-depth Developing AI Apps and Agents on Azure (AI-103) exam questions preparation is required to pass this Microsoft AI-103 exam. For the complete and comprehensive Developing AI Apps and Agents on Azure (AI-103) exam dumps preparation you can trust valid, updated, and AI-103 Questions which you can download from the PrepAwayPDF platform quickly and easily.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
>> Reliable Microsoft AI-103 Test Labs <<
The AI-103 exam is highly competitive and acing it is not a piece of cake for majority of the people. It requires a great skill set and deep knowledge AI-103 Exam Questions. An aspirant achieving Developing AI Apps and Agents on Azure (AI-103) certificate truly reflects his hard work and consistent struggle. These AI-103 exam practice test a person's true capacities and passing it requires extensive knowledge of each AI-103 topic.
NEW QUESTION # 95
Hotspot Question
Your company is piloting a customer support agent in a Microsoft Foundry project name Project1.
Project1 is connected to an existing Application Insights resource, and the company's support team reviews runs in the Traces tab.
The Foundry Agent Service is configured to perform the following actions:
- Retrieve the Application Insights connection string by calling
project_client.telemetry.get_application_insights_connection_string().
- Call configure_azure_monitor(connection_string=...) to enable
telemetry.
A separate LangChain service is configured to use OpenTelemetry and has the following configurations:
- Uses AzureAIOpenTelemetryTracer(connection_string=...,
enable_content_recording=False)
- Passes the tracer by using config={"callbacks":[azure_tracer]}
Company policy has the following requirements:
- Telemetry from LangChain and OpenTelemetry must be distinguishable
within the same Application Insights resource.
- Secrets and credentials must NOT be stored in prompts, tool
arguments, or span attributes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 96
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
Answer: B
NEW QUESTION # 97
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 Agent1 to answer customer questions about only the Contoso products.
The solution must meet the business requirements. What should you do?
Answer: B
Explanation:
Scenario: Contoso identifies the following business requirements:
Agent1 must answer questions only about the products sold by Contoso.
System Message Instructions: This dictates the agent's persona, boundaries, and rules. By explicitly configuring the system prompt (e.g., instructing the agent: "You are a product assistant.
Only answer questions using the provided product detail sheets. If you do not know the answer based on the provided documents, state that you do not know"), you prevent the model from answering with its general pre-trained knowledge.
Incorrect:
[Not B]
Few-shot examples: While useful for enforcing output formats or response tone, this does not explicitly stop a model from retrieving outside knowledge on unfamiliar topics.
[Not C, Not D]
Temperature & top-p: These parameters control the creativity and randomness of the model's responses. Decreasing them makes the model's outputs more deterministic and factual, but they do not actively restrict the model from generating information outside of the intended domain.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-safety-system-messages-in-azure-ai-studio-and-azure-openai-studio/4146991
NEW QUESTION # 98
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 most appropriate solution is Azure Content Understanding in Foundry Tools.
The Azure Content Understanding service natively combines advanced Optical Character Recognition (OCR), deep layout analysis, and pre-built generative capabilities. It handles varied document structures, multi-page tables, and template-generalizing field extraction without requiring custom machine learning model training. It operates as a low-administration, out-of-the- box solution perfectly aligned with document intelligence needs.
Incorrect:
[Not A]
Azure Language in Foundry Tools: Azure AI Language focuses primarily on unstructured text analytics, sentiment analysis, text summarization, and conversational capabilities. It lacks the built-in document layout analysis, table parsing, and visual OCR capabilities necessary to process complex scanned PDF invoice structures.
[Not C]
Azure Machine Learning model: Building, training, deploying, and managing a custom model in Azure Machine Learning requires significant data science expertise. This approach introduces high administrative overhead, complex infrastructure management, and manual pipeline maintenance, which violates the requirement for low administration.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5706482/azure-document-intelligence-and-content-understand
NEW QUESTION # 99
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
Answer: B
Explanation:
To remedy the "invalid project ID" error when calling the Azure Speech to Text REST API within your Microsoft AI Foundry project, you must update the project property in your API request body from a plain string name/ID to the fully qualified Azure Resource URI of the project.
The Speech to Text REST API (v3.0 and later) strictly expects resource links formatted as URIs rather than individual ID strings.
*-> Step 1: Construct the Correct Project URI
You need to pass the project as an absolute object reference. Construct your project property value using the following format:text
https://<your-region>://<your-project-guid>
Use code with caution.Replace <your-region> with your actual Azure Speech resource region (e.g., eastus, westeurope).Replace <your-project-guid> with the actual system-generated Unique Identifier of your project.
Step 2: Retrieve your Project GUID
Step 3: Update your Agent / REST API Request Body
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/azure-ai-speech
NEW QUESTION # 100
......
PrepAwayPDF has special training tools for Microsoft certification AI-103 exam, which can make you do not need to spend a lot of time and money but can get a lot of knowledge of IT technology to enhance your skills in a short time. And soon you will be able to prove your expertise knowledge and technology in IT industry. PrepAwayPDF's training courses for Microsoft Certification AI-103 Exam is developed by the study of PrepAwayPDF experts team to use their knowledge and experience.
Latest AI-103 Dumps Files: https://www.prepawaypdf.com/Microsoft/AI-103-practice-exam-dumps.html
BONUS!!! Download part of PrepAwayPDF AI-103 dumps for free: https://drive.google.com/open?id=1ZRyRKb2WcOd8Sobq12Xy38yOjDt0Bykz