Microsoft Developing AI Apps and Agents on Azure AI-103 Dumps in PDF

Free Microsoft AI-103 Real Questions (page: 3)


Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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.

HOTSPOT (Drag and Drop is not supported)
You have a Microsoft Foundry project that contains an internal Q&A agent.
Users report the following issues when they ask the agent questions:
▪ An increase in the following response: “No relevant information found”
▪ Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Model Availability rate and Provisioned Utilization.
Metrics to enable
To diagnose these issues, the Model Availability rate and Provisioned Utilization metrics are needed.
Problem 1 ("No relevant information found"): This points to an inference failure (RAG hallucination or retrieval gap), which is often caused by the model hallucinating when it is missing contextual grounding.
Problem 2 (HTTP 429 rate limit exceeded): This indicates resource limits. Monitoring provisioned utilization helps identify if your allocated capacity is simply maxing out during peak hours.
Box 2: RequestResponse
Diagnostic log to collect
To isolate whether your issues are caused by model unavailability, resource limits, or inference failures, you need to collect and analyze the RequestResponse diagnostic log category (also referred to as
RequestResponseLogs).
While your enabled metrics provide high-level context (e.g., Model Availability highlights backend service drops and Provisioned Utilization highlights capacity strain), they do not reveal per-request details. The
RequestResponse log records the exact HTTP status codes, error codes, and backend latency for every transaction.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/monitor-models




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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 have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase. You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?

  1. latency
  2. evaluation metrics
  3. run success rate
  4. token usage

Answer(s): D

Explanation:

To identify whether model input, model output, or expanded tool usage is driving the cost spike, you should focus on token usage.
Granular tracking of token usage breaks down the costs directly:
Input vs. Output Tokens: Tracing token usage isolates whether the user prompt (input) has grown or if the model is generating longer, more expensive responses (output).
Tool Calling: Expanded tool usage consumes extra tokens each time the agent schema processes an execution loop, which will clearly reflect as inflated token counts.


Reference:

https://www.mindstudio.ai/blog/subtraction-principle-agent-harness-optimization




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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.

HOTSPOT (Drag and Drop is not supported)
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
-▪ Each workflow run must include a retrieval step before generating a response.
-▪ Tool calls must authenticate by using the published agent’s own identity.
-▪ Tool access must use an identity isolated from other project resources.
▪ Tool access must use support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: required
Tool choice
To comply with your strict workflow and compliance requirements, you set tool_choice parameter required.
Setting tool_choice to required mathematically forces the model to invoke at least one tool (your internal retrieval mechanism) during the turn before it can finalize its answer.
Incorrect
[Not auto]
Setting tool_choice to auto hands over absolute decision-making power to the underlying foundational model. If the model determines it already "knows" the answer from its training data, it can skip the retrieval step entirely.
Box 2: Using a distinct agent identity bound to the client application
Configure the tool to authenticate by
Restriction: Use Published Agent's Own Identity
Map the agent to a Microsoft Entra Agent Identity. This gives the published agent its own distinct service principal identity within Entra ID, ensuring it does not inherit or use user tokens or secondary administrative keys.
Restriction: Identity Isolated from Project Resources
Rather than sharing the default Microsoft Foundry project managed identity (project-mi), assign a unique
User-Assigned Managed Identity exclusively to this agent instance. Limit its role-based access control (RBAC)
scopes solely to the specific retrieval indexes and the target external API endpoints.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-catalog




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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 have a Microsoft Foundry project named Project1 that contains the following:
▪ An OpenAPI tool that calls an external API
▪ A project connection named Connection1 that stores the API key of the external API
When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API
key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?

  1. Enable identity passthrough so that the tool uses the Microsoft Entra token of the caller.
  2. Add the API key header manually to the OpenAPI specification.
  3. Configure the tool to use the default connection of Project1.
  4. Connect the tool to Connection1.

Answer(s): D




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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 have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
▪ Some requests take more than 15 seconds to complete.
▪ Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls,
tool invocations, and timing information.
Which observability capability should you use?

  1. token usage
  2. monitoring
  3. safety metrics
  4. tracing

Answer(s): D

Explanation:

Here is LLM tracing (also known as trace view or distributed tracing for GenAI) needed.
Tracks Execution Flow: It captures the exact ordered sequence of LLM calls and tool invocations.
Pinpoints Latency: It provides timestamps and durations for every individual step to catch the 15-second bottlenecks.
Inspects Inputs/Outputs: It lets you see the exact payload sent to and from the knowledge API and the final
LLM prompt to find out why the agent hallucinated or ignored the data.


Reference:

https://coralogix.com/ai-blog/advanced-techniques-for-monitoring-traces-in-ai-workflows/




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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.

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 multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for user prompts.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): B




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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.

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 multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): B




Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the “Question” button to return to the question.
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.

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 multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for documents.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.


Reference:

https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection



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A
AI Tutor Explanation
6/13/2026 3:10:11 AM

Question 23:
Question 23 describes a multimodal model where users can upload unsafe images that could contain hidden instructions. The goal is to implement controls to mitigate this risk.
Key points to understand

  • Prompt shield for documents: Highly effective. It scans text extracted from inputs (including image text) before it’s sent to the LLM to catch hidden instructions or jailbreaking tries embedded in documents or image-derived text.
  • Prompt shield for user prompts: Partially effective. It blocks direct jailbreak attempts written in the user’s prompt, but doesn’t catch everything, especially content coming from image text.
  • Image moderation: Highly effective. Blocks unsafe or harmful images before they reach the model, preventing many attacks at the source.
  • Protected Material Detection: Not helpful here. It’s designed to detect copyrighted material in outputs, not to protect against inputs that try to manipulate the model.

Why this matters
  • The strongest defense is defense in depth: combine image moderation with both types of prompt shields. The document/text shield catches hidden instructions in extracted image text; the user-prompt shield mitigates jailbreak attempts in user-provided prompts; image moderation stops unsafe images before processing.

On the provided solution note
  • The stated answer (A: “configure a prompt shield for user prompts”) would help, but it alone does not fully meet the goal. A more robust approach is to apply all three controls (document prompt shield, user prompt shield, and image moderation) to achieve stronger risk mitigation.

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