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Microsoft AI-103 Exam Prep
Developing AI Apps and Agents on Azure (Page 3 )

Updated On: 3-Oct-2026
Page 3 of 39
PDF with 187 Questions
View Related Case Study

HOTSPOT (Drag and Drop is not supported)
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
▪ Authenticates by using a Microsoft Entra managed identity
▪ Sends prompts to a deployed model by using the Azure OpenAI Responses API
How should you complete the Python code? 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: DefaultAzureCredential
The azure.identity package provides DefaultAzureCredential, which automatically evaluates the environment to find a valid authentication mechanism.
When deployed in Azure, it natively picks up the configured Microsoft
Entra managed identity without requiring hardcoded secrets.
Box 2: create
The get_openai_client() context manager returns an OpenAI-compatible instance pre-configured for your project endpoint. To generate responses using Microsoft Foundry's Responses API protocol, you interact with the .responses resource and call its create method.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview



View Related Case Study

HOTSPOT (Drag and Drop is not supported)
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.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Not(IsBlank(Topic.Var01)
An if/else condition expression that ensures that Var01 contains a value
To validate that a local variable has a value in Microsoft Foundry workflows (which use the Power Fx expression language), the most adequate expression is:
If(Not(IsBlank(Topic.VariableName)), "Has Value Action", "Is Blank Action")
Expression BreakdownTopic.
VariableName: Refers to a local variable scoped to the current workflow topic.
IsBlank(): Checks if the variable is completely null or contains an empty string ("").
Not(): Inverts the boolean result so that a variable with contents evaluates to true.
If(): Executes the first action path if the condition is true (contains a value) and the second action path if it is false (blank).
Box 2: {Upper(Local.Var01)}
A Send message expression that returns the stored user response in uppercase
The adequate Power Fx expression to use in the Message to send area is {Upper(Local.Var01)}.
Key Implementation Rules
Variable Scope Prefix:
In Microsoft Foundry Workflows, all variables defined within the scope of the workflow must use the Local.
prefix.Uppercase Function:
The Upper() function converts text strings to all capital letters.
Message Interpolation: In a Send message node, Power Fx expressions must be enclosed in curly braces { } to be dynamically evaluated at runtime.


Reference:

https://learn.microsoft.com/sk-sk/microsoft-copilot-studio/advanced-power-fx



View Related Case Study

HOTSPOT (Drag and Drop is not supported)
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent
Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob
URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure AI Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? 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: Select user input, output, tool response, and Tool call and set Action to Block.
Guardrails
The correct configuration is option is: Select user input, output, tool response, and Tool call and set Action to
Block.
Points Coverage:
Microsoft Foundry Guardrails support four key intervention points: User input, Tool call, Tool response, and
Output. To thoroughly secure an agent run from harmful inputs, hidden instructions, or generated vulnerabilities, every stage of the lifecycle must be scanned.
Tool Call and Tool Response:
Because the customer support agent uploads files and processes blob URLs dynamically via a ticketing tool,
evaluating both Tool call (what the agent sends) and PM/Tool response (what the tool sends back to the agent)
is critical to prevent indirect prompt injections or malicious content processing at runtime.
Action Mode:
Setting the action to Block ensures that if any harmful content or policy violation is detected at any point in the cycle, the application aggressively prevents it from executing or returning to the user, satisfying the strict safety mandate.
Incorrect:
* Select Tool call and set Action to Block
Selecting only Tool call leaves the primary user prompt, tool output, and final agent response completely unmoderated. Furthermore, "Black" is not a valid configuration action.
* Select user input and Output and Set action to Annotate and
* Select user input and Tool response and set Action to Annotate
Setting the action to Annotate flags or labels content with safety metadata/severity scores but does not proactively block the run or prevent harmful content from being returned. Additionally, these options omit necessary intervention points (such as Tool call), violating the security constraints of the agentic workflow.
Box 2: A system-assigned managed identity that is assigned the Storage Blob Data Reader role
Storage access
To best configure storage access for image moderation, you should enable a system-assigned managed identity on the Azure AI Content Safety instance and assign it the Storage Blob Data Reader role on the Azure
Storage account. This eliminates the security risk of passing raw storage account keys or generating insecure
Shared Access Signature (SAS) tokens during agent execution.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/guardrails/guardrails-overview https://medium.com/@meetalpa/step-by-step-guide-to-securing-azure-ai-models-with-managed-identity-fba1ab cc0eed



View Related Case Study

You have a Microsoft Foundry project that contains three agents as shown in the following table.

You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
▪ Support a deterministic, step-based process that uses conditional branching and shared state across the agents.
▪ Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?

  1. a workflow
  2. threads and runs without a workflow
  3. a multi-agent group chat session
  4. separate agent runs coordinated in the application code

Answer(s): A

Explanation:

To fulfill your requirements while keeping development effort to an absolute minimum, you should leverage the native Microsoft Foundry Multi-Agent Workflows feature (built directly into the Foundry Agent Service and managed via the Foundry portal visual editor or declarative YAML files).Using this visual, low-code orchestration layer removes the need to write custom graph routing logic, state managers, or manual handoffs in code.
The minimum required architecture and features that must be included in your solution are structured below.
1. The Orchestration Layer: Declarative Workflow
Instead of writing a code-first orchestrator, you must define a Foundry Workflow Definition (YAML or Visual).
2. State Management: Shared Context Variables
3. Agent Configuration & Native Tooling


Reference:

https://devblogs.microsoft.com/foundry/introducing-multi-agent-workflows-in-foundry-agent-service/



View Related Case Study

You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?

  1. the project URL
  2. the custom speech project ID
  3. the project ID
  4. the custom speech endpoint URL

Answer(s): B



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