Online Questions – Valid Practice To your AI-103 Exam (Updated 159 Questions) [Q60-Q78]

NEW QUESTION 60
You have a Docker host named Host! that contains a container base image.
You have an Azure subscription that contains a custom speech-to-text model named model1.
You need to run model 1 on Host1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

NEW QUESTION 61
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of all the files that can be uploaded to the service?

NEW QUESTION 62
You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
– Complex questions must retrieve information from multiple chunks.
– Multi-turn conversations must influence retrieval planning.
– Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?

NEW QUESTION 63
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 64
Drag and Drop Question
You are developing an application that will detect faulty components produced on a factory production line. The components are specific to your business.
You need to use the Azure Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

NEW QUESTION 65
Hotspot Question
You have an Azure subscription.
You need to create a new resource that will generate fictional stores in response to user prompts.
The solution must ensure that the resource uses a customer-managed key to protect data.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 66
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?

NEW QUESTION 67
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?

NEW QUESTION 68
You are building an app that will share user images. You need to configure the app to:
* Categorize each image as a photograph or drawing.
* Generate a caption for the image.
* Minimize development effort.
Which two services should you include?

NEW QUESTION 69
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.

NEW QUESTION 70
You have an Azure subscription that contains an Azure AI Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure AI Content Understanding project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?

NEW QUESTION 71
You have a factory that contains a production line.
You need to develop a solution that will analyze a video feed to detect and track the movement of employees as they work on the production line.
Which service should you include in the solution?

NEW QUESTION 72
Hotspot Question
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
– An if/else condition expression that ensures that Var01 contains a
value
– A Send message expression that returns the stored user response in
uppercase
How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 73
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?

NEW QUESTION 74
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the max_tokensparameter.
Does this meet the goal?

NEW QUESTION 75
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.

NEW QUESTION 76
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 an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

NEW QUESTION 77
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
– Interpret written and spoken questions from users.
– Generate answers to the questions.
– Output the answers as speech.
You need to create the project for the agent.
What should you use?

NEW QUESTION 78
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents.
Each agent will access the same Azure AI Search resource.
You need to recommend a solution to centrally manage the Azure AI Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?