AI-102 Implement agentic AI solutions Practice Test 2 — 15 Questions

Question 1

A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?

Accepted Answer

Use the Bot Framework SDK to build the bot with Teams-specific features.. Option A is correct because the Bot Framework SDK provides the Teams-specific activities, Adaptive Cards, and middleware needed to build an agent that works properly inside Microsoft Teams. Option B is correct because a Teams app manifest (with the bot's ID, scopes, and commands) must be packaged and uploaded/sideloaded so Teams knows how to surface the bot to users. Option C is correct because registering the bot in Azure Bot Service yields the Microsoft App ID and password that the bot uses to authenticate with the Bot Connector and Teams channel. Option D is not required: bot code can run on App Service, containers, or Functions, so deploying to Azure Functions is only one optional hosting choice. Option E is not required: the Bot Framework SDK supports multiple languages (C#, JavaScript, Python, Java), so writing in C# with the Azure SDK for .NET is not a mandatory step.

Answer

Deploy the bot code to an Azure Function.

Answer

Write the bot in C# using Azure SDK for .NET.

Question 2

An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?

Accepted Answer

Pre-process the text to handle negations and sarcasm.. Option A is correct because Azure AI Language's prebuilt sentiment analysis can misclassify negations (e.g., "not good") and sarcasm, so pre-processing the text to normalize or flag these constructs improves accuracy. Option B is correct because training a custom sentiment analysis model on domain-specific labeled data lets the service learn industry-specific vocabulary and phrasing, which typically boosts accuracy for specialized feedback. Option C is not appropriate because simply swapping language models without fine-tuning does not address domain-specific or negation/sarcasm issues. Option D is wrong because the number of decimal places in the score is a formatting detail and does not affect model accuracy. Option E is wrong because raising a positive-sentiment confidence threshold only changes classification cutoffs and does not improve the underlying sentiment detection for negative feedback.

Answer

Switch to a different language model without fine-tuning.

Answer

Increase the number of decimal places in the sentiment score.

Answer

Increase the confidence threshold for positive sentiment.

Question 3

You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?

Accepted Answer

Change chunking to use semantic boundaries: split at clause or section headings.. Splitting contracts at semantic boundaries (clause or section headings) preserves the natural meaning and context of each chunk, which is critical for legal document analysis. Page-level chunking often splits a clause across two pages, causing the vector search to retrieve incomplete or irrelevant information. By aligning chunks with the document's logical structure, the RAG pattern retrieves more coherent and relevant passages for the Azure OpenAI agent to process.

Answer

Keep page-level chunking but add 50% overlap between chunks.

Answer

Use a different embedding model, such as text-embedding-3-large.

Answer

Increase the chunk size to 5 pages per chunk and reduce overlap.

Question 4

Drag and drop the steps to implement an Azure AI Bot Service with QnA Maker into the correct order.

Accepted Answer

Create and publish a QnA Maker knowledge base, then create an Azure Bot Service bot, then connect the bot to the QnA Maker knowledge base, then test the bot.. Start with QnA Maker, build the knowledge base, create the bot, connect it, and test.

Answer

Create an Azure Bot Service bot, then create and publish a QnA Maker knowledge base, then connect the bot to the QnA Maker knowledge base, then test the bot.

Answer

Create and publish a QnA Maker knowledge base, then create an Azure Bot Service bot, then test the bot, then connect the bot to the QnA Maker knowledge base.

Answer

Create an Azure Bot Service bot, then create and publish a QnA Maker knowledge base, then test the bot, then connect the bot to the QnA Maker knowledge base.

Question 5

A company is building an agent using Azure AI Foundry Agent Service. The agent must be able to call an external REST API that returns real-time inventory data. The API requires an OAuth 2.0 token that changes frequently. Which approach should the team use to enable the agent to call this API securely?

Accepted Answer

Use a function tool in the agent definition that invokes an Azure Function, which retrieves the token from Azure Key Vault and calls the REST API.. The correct approach is to use a function tool that invokes an Azure Function, which securely retrieves the OAuth token from Azure Key Vault and calls the REST API. This keeps credentials out of the agent and handles token refresh. Other options either expose the token, rely on unsupported authentication, or misuse the knowledge base.

Answer

Configure the agent to use a managed identity and assign it the necessary permissions to call the REST API directly.

Answer

Store the OAuth token in the agent's knowledge base and have the agent retrieve it when needed.

Answer

Embed the OAuth token in the agent's system prompt so the agent can include it in API calls.

Question 6

A company is building an agent by using Azure AI Foundry Agent Service. The agent must use a file search tool that references a 400-page product manual stored as an uploaded file. The team wants the agent to retrieve relevant passages without you writing chunking or embedding code. Which tool should you configure?

Accepted Answer

The built-in file_search tool attached to the agent, with the manual uploaded to the agent's vector store. The requirement is managed retrieval over an uploaded document without custom chunking or embedding work. The built-in file_search tool ingests uploaded files into a vector store, handles chunking and embedding automatically, and returns relevant passages to the model during runs. Building an Azure AI Search index, writing a function tool, or using code interpreter all shift ingestion and retrieval responsibility back to the team, which the scenario rules out.

Answer

A function tool that calls a Logic App workflow to parse the PDF at query time

Answer

Azure AI Search index configured as a connected knowledge source with semantic ranker enabled

Answer

A code interpreter tool with the PDF mounted in the session file store

Question 7

A company is using Azure AI Foundry to create an agent that must answer questions based on a set of internal documents. The agent should provide accurate answers and cite the source documents. Which feature should the team use to ground the agent's responses in the documents?

Accepted Answer

Azure AI Search index connected as a knowledge tool in the agent.. Using an Azure AI Search index as a knowledge tool is the correct approach because it enables retrieval-augmented generation, allowing the agent to fetch relevant document chunks and cite sources. Fine-tuning, SAS URLs, and embedding documents in the prompt do not provide effective grounding or citation capabilities for a document library.

Answer

Fine-tuning the underlying language model with the internal documents.

Answer

Storing the documents in Azure Blob Storage and providing the agent with a SAS URL.

Answer

Embedding the documents in the agent's system prompt.

Question 8

A team is building an agent using Azure AI Foundry Agent Service that must use the function calling tool to interact with a custom API. The agent sometimes fails to call the function with the correct parameters. Which action should the team take to improve the reliability of function calling?

Accepted Answer

Provide detailed descriptions and examples for each function parameter in the function definition.. Providing detailed descriptions and examples for each function parameter is the correct action because it gives the model clear guidance on expected values, improving parameter accuracy. Other options either increase randomness, misuse tools, or reduce functionality without addressing the root cause of parameter errors.

Answer

Switch to using the code interpreter tool instead of function calling for API interactions.

Answer

Increase the temperature setting of the model to encourage more creative parameter generation.

Answer

Reduce the number of functions available to the agent to only one.

Question 9

A company is deploying an agent built with Azure AI Foundry Agent Service to production. The agent must log all interactions for auditing and compliance. The team needs to capture the full conversation history, including tool calls and their results. Which approach should they use?

Accepted Answer

Enable diagnostic settings on the Azure AI Foundry resource to send logs to Azure Monitor and Log Analytics.. Enabling diagnostic settings to send logs to Azure Monitor and Log Analytics is the correct approach because it provides a centralized, durable, and queryable audit trail of all agent interactions, including tool calls. Other methods are either local, manual, or incomplete, and do not meet compliance needs for production auditing.

Answer

Use the agent's built-in conversation history feature and periodically export it manually via the portal.

Answer

Configure the agent to write conversation logs to a local file on the client application.

Answer

Instruct the agent to include a summary of each interaction in its response for the user to save.

Question 10

A team is building an agent with Azure AI Foundry Agent Service that must call several internal function tools. The team reports that the model sometimes invents function names that do not exist and passes arguments that do not match the tool schema. Which TWO practices should the team adopt to reduce these failures? (Choose two.)

Accepted Answer

Handle tool-call validation errors by returning a structured error message to the model and allowing it to retry. Reliable function calling depends on a precise contract and a recovery path. Clear names, descriptions, and JSON schemas tell the model exactly which tools exist and how to call them, while returning structured validation errors lets the model self-correct on a subsequent turn. Temperature, parallel-call settings, and token limits affect other behaviors and do not resolve hallucinated function names or malformed arguments.

Answer

Disable parallel tool calls so the model can only invoke one function per turn

Answer

Set the agent's temperature to a high value so the model explores more tool combinations

Answer

Increase the model's max tokens so it has more room to explain each function call

Question 11

A bank is deploying an Azure AI Foundry agent that provides account balance information over the phone. The agent must authenticate callers by using voice biometrics before revealing any account details. The bank wants to integrate this authentication step into the agent's conversation flow without exposing sensitive data to the language model. Which approach should the bank use?

Accepted Answer

Add a voice biometrics tool to the agent and configure a pre-action that runs before any account-related tool call.. The bank needs voice biometric authentication integrated into the agent flow without exposing sensitive data to the language model. A voice biometrics tool with a pre-action that gates account-related tool calls achieves this: the biometric check happens outside the model, and only a success token is passed to the orchestration layer, allowing account tools to run. This satisfies security and integration requirements.

Answer

Include the caller's voiceprint in the system message so the model can compare it to the live audio.

Answer

Use Azure Communication Services to record the call and send the audio to the agent as a text transcript for authentication.

Answer

Configure the agent to ask for the caller's account number and PIN, then validate them against a database before answering.

Question 12

A developer is creating an agent in Azure AI Foundry Agent Service and wants the agent to remember a user's stated preferences across separate conversations that occur days apart. The agent definition already includes a model deployment and instructions. What should the developer add to persist this context?

Accepted Answer

Attach a Foundry memory store to the agent so it can save and retrieve user facts across threads. Cross-conversation memory requires a durable, user-scoped store outside the model and outside static agent configuration. Attaching a Foundry memory store lets the agent save facts during one thread and retrieve them in later threads for the same user. A larger context window, code interpreter session files, and the instructions field all fail because they are either ephemeral, shared, or scoped to a single run.

Answer

Increase the model's context window by selecting a larger deployment

Answer

Enable the code interpreter tool so the agent can write preferences to a file

Answer

Add the preferences to the agent's instructions field in the agent definition

Question 13

A media company is building an Azure AI Foundry agent that generates summaries of news articles. The agent uses a tool to fetch articles from an internal CMS. The company wants to ensure the agent respects content usage rights and does not summarize articles that are marked as restricted. The agent must also log every article it accesses for auditing. Which two actions should the team take? (Choose two.)

Accepted Answer

Enable diagnostic logging on the Azure AI Foundry resource to capture all tool calls and their responses.. To respect usage rights, the CMS tool should filter articles based on the usage rights field before they reach the agent, ensuring only permissible content is summarized. To audit access, diagnostic logging on the Azure AI Foundry resource captures all tool calls and responses. Together, these actions enforce policy at the data layer and provide a verifiable audit trail without exposing restricted content to the model.

Answer

Instruct the agent in its system message to ignore articles that are marked restricted.

Answer

Use a content filter to block articles that contain certain keywords associated with restricted content.

Answer

Store a copy of every article in the agent's conversation history for later review.

Question 14

An agent built with Azure AI Foundry Agent Service performs a long-running operation by calling a function tool that starts a batch job. The batch job can take up to 30 minutes, and the agent currently times out because the function tool waits for completion. The team wants the agent to continue the conversation after the job finishes. Which pattern should the team implement?

Accepted Answer

Have the function tool return immediately with a job identifier, then resume the agent when the job completes via a new run or thread message. Long-running work should be decoupled from the agent run. Returning a job identifier immediately keeps the tool call fast, and an external completion event can then deliver the result back to the agent by starting a new run or posting to the thread. Blocking the tool call, moving the job into code interpreter, or enabling parallel calls all fail to provide a reliable continuation path after the job finishes.

Answer

Increase the function tool's HTTP client timeout to 30 minutes so the call waits for the job

Answer

Move the batch job into the code interpreter tool so it runs inside the agent's session

Answer

Enable parallel tool calls so the agent can start the job and keep responding at the same time

Question 15

A retail company is creating an Azure AI Foundry agent that helps customers find products. The agent must be able to call a product search API and a store inventory API. The team wants to define these capabilities so the agent can invoke them when needed. What should the team do?

Accepted Answer

Add the two APIs as tools in the agent definition, providing their OpenAPI schemas.. The team needs to enable the agent to call two external APIs. In Azure AI Foundry, tools are the mechanism for integrating external services. By adding each API as a tool with its OpenAPI schema, the agent gains the ability to invoke them with correct parameters and handle responses. This is the native, supported approach and requires minimal custom code.

Answer

Embed the API endpoints in the agent's system message and instruct the model to call them by using HTTP requests.

Answer

Use the agent's built-in code interpreter to write Python code that calls the APIs.

Answer

Create an Azure Function for each API and configure the agent to use them as skills.