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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 2: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
>> Latest AI-103 Exam Objectives <<
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NEW QUESTION # 95
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.
Answer:
Explanation:
Explanation:
If/else condition expression: Not(IsBlank(Local.Var01))
Send message expression: {Upper(Local.Var01)}
The correct if/else condition is Not(IsBlank(Local.Var01)) because the workflow must continue only when the Ask a question node has captured a nonblank value. In Power Fx, IsBlank checks whether a value is blank, while Not reverses that Boolean result; therefore, Not(IsBlank(Local.Var01)) evaluates to true only when Var01 contains user input. IsEmpty(Local.Var01) is not the correct choice because IsEmpty checks whether a table contains records, not whether a text variable has a value. The Power Fx formula reference distinguishes these functions by defining IsBlank for blank values and IsEmpty for empty tables.
The Send message expression must be {Upper(Local.Var01)}. Microsoft Foundry workflow guidance uses the same pattern for an Ask a question node saved as Var01, followed by a Send message action that outputs
{Upper(Local.Var01)}. The Local. prefix is required because the response is stored in a local workflow variable, and Upper() converts the stored text response to uppercase. Reference topics: Microsoft Foundry workflows, Power Fx expressions, local variables, if/else branching, and Send message actions.
NEW QUESTION # 96
You need to optimize content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Moderate text content feature to run the tests.
Does this meet the requirement?
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Yes. Moderate text content is exactly the Content Safety testing workflow Microsoft documents for analyzing sample text and tuning harmful-content filtering.
Microsoft ' s current quickstart instructs users to open the Moderate text content panel, provide sample text, and select Run test . The service returns detected harm categories together with severity levels. The user can then open Configure filters , adjust which severity levels are allowed or prohibited for each category, and run the text again to verify the behavior. Microsoft Learn That directly satisfies the requirement to optimize the content-filter configuration by testing sample questions.
The Safety metaprompt capability influences generative model behavior but does not provide the same moderation-threshold testing. Protected material detection evaluates a separate protected-content concern and likewise is not the harmful-content filter test surface.
Therefore, the answer printed in the supplied document ( B, No ) conflicts with current Microsoft Learn documentation. The verified answer is A, Yes .
Study Guide alignment: configure safety filters, guardrails, risk detection, content moderation, and safety evaluations for generative AI and agentic systems . Microsoft Learn
NEW QUESTION # 97
You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?
Answer: D
Explanation:
To configure end-to-end visibility into each step of the request workflow, you should Enable application tracing in the project.
Project-Level Observability: Enabling application tracing allows Microsoft Foundry to natively track, trace, and monitor every component of the workflow-including calls to the Azure OpenAI Responses API and associated tools like Azure AI Content Safety-within the same request without requiring complex, separate SDK configurations.
Centralized Tracking: Tracing captures the exact execution path across the project endpoints, allowing you to see metrics, latency, and inputs/outputs sequentially for both the model invocation and the content safety check.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/trace-production-sdk
NEW QUESTION # 98
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
Answer: C
Explanation:
To resolve the HTTP 403 Forbidden errors when making inference calls via the Azure OpenAI v1 API and DefaultAzureCredential, users must be assigned the Cognitive Services OpenAI User built-in Azure RBAC role.
Why This Happens
Running az login successfully authenticates the user with Microsoft Entra ID, but it does not grant data-plane access permissions. By default, standard control-plane roles (like Reader or Foundry User) only allow users to view project metadata or manage settings, not send prompts to the model deployment endpoint itself.
Recommended Role Definition
Role Name: Cognitive Services OpenAI User
Permissions Granted: This role provides the absolute minimum privileges required to execute chat completions, embeddings, and general inference tasks (Microsoft.CognitiveServices/accounts/OpenAI/deployments/search/action and Microsoft.CognitiveServices/accounts/OpenAI/deployments/causalLanguageModeling/action). It does not allow users to deploy new models, view access keys, or alter configurations Scope Placement: Assign this role to the users (or a Microsoft Entra ID Group) at the Azure OpenAI resource level or the Resource Group level containing your Microsoft Foundry infrastructure.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/managed-identity
NEW QUESTION # 99
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 each file that can be uploaded to the service?
Answer: A
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
NEW QUESTION # 100
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