AI-102 Implement an agentic solution Practice Test 3 — 15 Questions

Question 1

You are building an agentic solution using Azure AI Agent Service. The agent needs to send an email via Microsoft Graph API. Which authentication method should you use for the action?

Accepted Answer

OAuth 2.0. Microsoft Graph API requires OAuth 2.0 for authentication because it uses delegated or application permissions to access user data securely. Azure AI Agent Service can use OAuth 2.0 with a managed identity or service principal to obtain an access token for the Graph API, ensuring proper authorization and compliance with Microsoft's security model.

Answer

Client Certificate

Answer

API Key

Answer

Basic Authentication

Question 2

You deploy an agent in Microsoft Foundry that uses a custom skill in Azure AI Search. The skill calls an Azure Function to enrich documents. The function uses an API key. The deployment succeeds but the skill returns an error when processing documents. The function logs show the request is received but the API key is missing. What is the most likely cause?

Accepted Answer

The skill definition does not include the apiKey in the header.. The custom skill definition in Azure AI Search must include an `apiKey` property in the header to authenticate requests to the Azure Function. Since the function logs show the request is received but the API key is missing, the most likely cause is that the skill definition omitted the `apiKey` header. This is a common configuration error when setting up custom skills with HTTP-triggered functions.

Answer

The Azure Function is not running.

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The API key stored in Azure Key Vault is expired.

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The custom skill's URI is incorrect.

Question 3

You are building an agentic solution using Azure AI Agent Service. The agent needs to send an email via Microsoft Graph API. Which authentication method should you use for the action?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Code Interpreter. (Code Interpreter) is correct because Azure AI Agent Service's Code Interpreter tool can execute Python code that uses libraries like `pyodbc` or `pymssql` to connect to a SQL database, run dynamic SQL queries based on user input, and return results. This allows the agent to retrieve information from a SQL database dynamically without requiring pre-defined function schemas or external API calls.

Answer

Function calling

Answer

Grounding with Bing

Answer

Kusto Query Language (KQL)

Question 4

You are developing an agentic solution that uses multiple AI agents to collaborate on a complex task. To ensure the agents work together effectively, you need to define a clear handoff protocol. Which approach should you use in Azure AI Agent Service to enable agent-to-agent communication?

Accepted Answer

Use Agent Handoff feature. Azure AI Agent Service provides a built-in Agent Handoff feature that enables seamless agent-to-agent communication by defining a structured handoff protocol. This feature allows agents to pass tasks and context to each other without custom code, ensuring efficient collaboration in multi-agent systems.

Answer

Configure a shared memory store

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Implement a custom API for agent communication

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Orchestrate agents sequentially using a script

Question 5

You are deploying an agentic solution using Azure AI Agent Service. The agent needs to be invoked from a custom application using REST API calls. Which endpoint should you use to send a message to the agent?

Accepted Answer

POST /threads/{thread_id}/messages. To send a message to an existing conversation thread in Azure AI Agent Service, you must use the POST /threads/{thread_id}/messages endpoint. This adds the user's message to the specified thread, which the agent can then process in a subsequent run. The REST API requires the thread to already exist, and messages are posted directly to that thread's resource.

Answer

POST /threads/{thread_id}/runs

Answer

POST /threads

Answer

GET /agents

Question 6

You are developing an agentic solution that requires the agent to maintain context across multiple turns in a conversation. Which feature should you use to store and retrieve conversation history?

Accepted Answer

Threads. In the context of agentic solutions on Azure, threads are the correct feature for storing and retrieving conversation history across multiple turns. Threads maintain a sequential record of messages and tool calls, allowing the agent to reference prior context and maintain coherent multi-turn interactions. Sessions, vector stores, and memory serve different purposes and do not natively preserve conversational context in the same structured way.

Answer

Sessions

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Vector store

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Memory

Question 7

You are building an agentic solution that needs to perform actions on behalf of the user, such as sending emails and updating calendars. Which authentication approach should you use for the agent to access Microsoft Graph API with delegated permissions?

Accepted Answer

OAuth 2.0 authorization code flow with PKCE. The authorization code flow with PKCE is the correct approach because the agent needs to act on behalf of a signed-in user, requiring delegated permissions. This flow securely exchanges an authorization code for an access token, and PKCE adds a cryptographic challenge to prevent interception attacks, making it ideal for public clients like agent applications.

Answer

OAuth 2.0 client credentials flow

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OAuth 2.0 implicit flow

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OAuth 2.0 device code flow

Question 8

You are designing an agentic solution that uses Azure AI Agent Service to answer customer support queries. The agent needs to retrieve information from a knowledge base stored in Azure AI Search. Which tool should you enable for the agent?

Accepted Answer

Knowledge base. The Knowledge base tool is the correct choice because it is specifically designed to connect an Azure AI Agent to an Azure AI Search index, enabling retrieval-augmented generation (RAG) from structured or unstructured knowledge sources. This tool allows the agent to query the search index and return relevant chunks of information to answer customer support queries without custom code.

Answer

Code Interpreter

Answer

Function calling

Answer

KQL

Question 9

You are troubleshooting an agentic solution where the agent is not returning responses within acceptable time limits. You suspect the agent is making too many sequential calls to external tools. Which strategy should you recommend to reduce latency?

Accepted Answer

Enable parallel tool execution. Enabling parallel tool execution allows the agent to invoke multiple external tools simultaneously rather than sequentially, directly reducing the total latency caused by serial tool calls. This is a core optimization in agentic frameworks like Semantic Kernel or AutoGen, where tool calls are independent and can be dispatched concurrently.

Answer

Increase the max token limit

Answer

Add more tools to distribute the load

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Reduce the thread history length

Question 10

You are developing an agentic solution that uses Azure AI Agent Service with a custom function calling tool. The agent needs to call a function that requires authentication to an external API. How should you securely pass the API key to the function?

Accepted Answer

Use Azure Key Vault to store the API key and reference it in the function. Azure Key Vault provides a secure, centralized service for storing and managing secrets like API keys. In Azure AI Agent Service, you can configure the function to retrieve the API key at runtime from Key Vault using managed identities, ensuring the key is never exposed in code, configuration, or prompts. This follows the principle of least privilege and aligns with Azure's security best practices for agentic solutions.

Answer

Hardcode the API key in the function code

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Store the API key in an environment variable

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Pass the API key as part of the agent's system prompt

Question 11

You are building an agentic solution that needs to process large documents uploaded by users. The agent should extract key information and summarize the content. Which tool should you enable?

Accepted Answer

Code Interpreter. Code Interpreter (now called 'Code Interpreter' in Azure AI Studio/Assistants API) provides a sandboxed Python environment that can execute code to process uploaded files, including parsing large documents, extracting key information, and generating summaries. It handles file I/O, data manipulation, and natural language processing libraries, making it the correct tool for this agentic document-processing task.

Answer

Function calling

Answer

KQL

Answer

Knowledge base

Question 12

You are designing an agentic solution using Azure AI Agent Service. The agent needs to be able to both read and write data to an Azure SQL database. Which TWO tools should you configure?

Accepted Answer

Function calling. Function calling is correct because it allows the agent to define custom functions that can execute SQL queries against Azure SQL Database, enabling both read and write operations through a structured API call pattern. Code Interpreter is correct because it can run Python code that uses libraries like pyodbc or SQLAlchemy to connect to Azure SQL Database and perform data manipulation, providing a sandboxed execution environment for dynamic SQL operations.

Answer

KQL

Answer

Grounding with Bing

Answer

Knowledge base

Question 13

You are deploying an agentic solution that must comply with data residency requirements. The agent processes personal data from users in the European Union. Which THREE actions should you take to ensure compliance?

Accepted Answer

Deploy the agent service in an Azure region within the European Union. Deploying the agent service in an Azure region within the European Union ensures that personal data is processed and stored in a location that meets EU data residency requirements. Azure's regional deployment guarantees that data does not leave the specified geographic boundary, which is essential for compliance with regulations like GDPR.

Answer

Disable logging for the agent to avoid storing personal data

Answer

Configure the agent's data storage to use a globally redundant storage account

Question 14

You are developing an agentic solution that uses multiple agents to handle customer inquiries. You need to ensure that agents can hand off to each other with full context. Which THREE features should you use?

Accepted Answer

Shared Memory. Shared Memory (B) is correct because it allows multiple agents to access and update a common data store, ensuring that when one agent hands off to another, the full conversation history and context are preserved. This is essential for maintaining continuity in multi-agent systems, as each agent can read the shared state to understand what has been discussed and decided.

Answer

KQL

Answer

Function calling

Question 15

Refer to the exhibit. You are configuring an agent in Azure AI Agent Service. You want the agent to be able to execute Python code and call a custom function to get weather data. What is the issue with the JSON configuration?

Accepted Answer

The model 'gpt-4o' is not supported. The Azure AI Agent Service currently does not support the gpt-4o model for assistants. The supported models include GPT-4, GPT-4-turbo, and GPT-4-32k. Using an unsupported model will cause the agent to fail during configuration or runtime.

Answer

The function definition is missing the 'strict' parameter

Answer

The 'code_interpreter' tool type should be 'code_interpreter' not 'code_interpreter'

Answer

The 'assistant_id' should be a thread ID