AI-102 Implement agentic AI solutions — All Questions With Answers

Question 1

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

Accepted Answer

Use a conversational agent framework like AutoGen with a tool that executes SQL queries, and maintain conversation state.. AutoGen is a conversational agent framework designed for multi-turn, stateful interactions. It can maintain conversation context across turns and integrate a tool to execute SQL queries, which directly meets the requirement for complex multi-turn conversations with context retention. The other options either lack state management or rely on stateless prompt engineering, which is insufficient for maintaining context in a multi-turn agent.

Answer

Use a single prompt that includes the entire conversation history and the latest user question, then generate SQL.

Answer

Use a chain-of-thought prompt to generate SQL queries directly from user input, without maintaining conversation history.

Answer

Use embeddings-based retrieval to find relevant past interactions and include them in the prompt.

Question 2

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

Accepted Answer

Azure AI Services deployed in a private endpoint with no data leaving the network.. Deploying Azure AI Services within a private endpoint, combined with ensuring no data leaves the network, aligns with HIPAA's requirement for data privacy and compliance. This configuration uses Azure Private Link to keep all traffic within the Microsoft backbone network, preventing exposure to the public internet and meeting the strict data residency and encryption standards mandated by HIPAA.

Answer

Microsoft Bot Framework SDK with a custom connector.

Answer

Azure Cognitive Search with custom analyzers.

Answer

Azure OpenAI Service with data processing enabled.

Question 3

A company is using Azure OpenAI Service to power a customer support agent. The agent sometimes generates incorrect information when it cannot find an answer in the knowledge base. The team wants to ensure the agent only responds using information from the knowledge base and explicitly states when it does not know the answer. Which configuration should the team use?

Accepted Answer

Use 'use your own data' feature with strict content filtering and set the model to only respond based on retrieved documents.. The 'use your own data' feature in Azure OpenAI Service allows you to restrict the model to answer only from the retrieved documents, ensuring responses are grounded in the knowledge base. Strict content filtering further prevents the model from generating unverified information, and the explicit setting to respond based solely on retrieved documents directly addresses the requirement to state when it does not know the answer.

Answer

Use a custom model fine-tuned on the knowledge base and disable content filtering.

Answer

Use a system message that says 'If you don't know, say you don't know' and rely on the model's training.

Answer

Use prompt engineering with a system message that instructs the model to only answer from the knowledge base, with no additional filtering.

Question 4

A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?

Accepted Answer

Train the entity extraction model with more examples of dates.. The custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.

Answer

Increase the training data for location entities.

Answer

Add a prebuilt entity component for date.

Answer

Use a different intent classification model.

Question 5

Match each Azure AI pricing tier to its description.

Accepted Answer

Free (F0): Provides limited transactions per month at no cost.. Common Azure Cognitive Services pricing tiers include Free (limited free transactions), Standard S0 (pay-as-you-go moderate), and Premium (enterprise high-throughput).

Answer

Free (F0): Pay-as-you-go pricing for moderate workloads.

Answer

Standard (S0): Designed for high-scale enterprise workloads with higher throughput.

Answer

Premium: Provides limited transactions per month at no cost.

Question 6

A team is implementing an agent using Azure AI Foundry Agent Service that must use the code interpreter tool to analyze uploaded CSV files and generate charts. The agent must ensure that the code interpreter runs in a secure, isolated environment and that file uploads are handled correctly. Which two actions should the team take? (Choose two.)

Accepted Answer

Upload the CSV files using the Azure AI Foundry file upload API and reference the returned file IDs in the agent's tool resources.. The team must enable the code interpreter tool and provide file IDs of uploaded CSV files, which requires uploading files via the Azure AI Foundry file upload API and referencing the file IDs in the agent's tool resources. These steps ensure the agent can run Python code on the files in a secure sandbox. Other options misconfigure file access or rely on unsupported settings.

Answer

Configure the agent to use the code interpreter tool with the 'auto' execution mode to allow the model to decide when to run code.

Answer

Ensure the agent's managed identity has the Storage Blob Data Contributor role on the Azure Storage account used by the code interpreter.

Answer

Upload the CSV files to the agent's knowledge base and configure the agent to retrieve them using vector search.

Question 7

A travel agency is building an Azure AI Foundry agent that helps employees book corporate trips. The agent must call a custom API named GetFlightAvailability to retrieve live flight data. The API requires an OAuth 2.0 access token that is refreshed every hour. The team wants to minimize development effort while ensuring the token is never exposed in the agent's chat transcript. What should the team configure?

Accepted Answer

Add the API as a tool in the agent definition and configure OAuth 2.0 authentication with a managed identity.. The agent must call an external API securely with OAuth 2.0 tokens that refresh hourly. Native tool definitions in Azure AI Foundry support authentication configurations, including OAuth 2.0 with managed identity, which handles token acquisition and refresh without exposing secrets. This keeps credentials out of the chat transcript and reduces custom development compared with proxies or flows.

Answer

Use a Power Automate flow to call the API with a stored connection, and invoke the flow from the agent as a tool.

Answer

Store the OAuth 2.0 token in the agent's system message and update it hourly by using a Logic App.

Answer

Create an Azure API Management instance that proxies the API and injects a subscription key, then call the proxy from the agent.

Question 8

An agent built with Azure AI Foundry Agent Service must call an internal REST API that requires an OAuth 2.0 token. The security team refuses to store long-lived secrets in the agent definition and wants short-lived, scoped credentials issued at run time. Which approach should you implement?

Accepted Answer

Register a connection in Azure AI Foundry that uses a managed identity to obtain tokens for the API's scope. The requirement is runtime issuance of short-lived, scoped credentials without storing secrets in the agent. A Foundry connection using a managed identity lets the platform acquire tokens for the target API's audience on demand, and Microsoft Entra ID governs rotation and scoping. Storing a client secret, referencing a vault URI in a prompt, or using the Graph endpoint all fail to deliver scoped, short-lived tokens for the internal API.

Answer

Add the API credentials to Azure Key Vault and reference the vault URI in the agent's system prompt

Answer

Store the API's client secret in the agent's environment variables and read it inside a function tool

Answer

Configure the agent's function tool to request a token from the Microsoft Graph token endpoint for the API's scope

Question 9

A logistics company uses an Azure AI Foundry agent to answer questions about shipment status. The agent has access to a tool that queries an internal tracking API. During testing, the agent sometimes responds with fabricated tracking numbers that do not exist in the system. The team wants to ensure the agent only returns tracking numbers that are present in the API response. Which action should the team take?

Accepted Answer

Enable the agent's grounding feature by configuring the tool output as a data source and instructing the agent to cite only from it.. The agent fabricates tracking numbers because it is not constrained to the API's returned data. Configuring the tool output as a grounding data source and instructing the agent to cite only from it ensures responses are based on real API results. This reduces hallucinations and directly enforces the requirement that only existing tracking numbers be returned.

Answer

Increase the temperature setting of the underlying model to encourage more creative responses.

Answer

Add a post-processing step that validates tracking numbers against a regular expression and removes invalid ones.

Answer

Reduce the model's max tokens setting so that the agent cannot generate long responses.

Question 10

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

Accepted Answer

Azure Bot Service. Azure Bot Service is the correct choice because it provides a managed environment for building, deploying, and scaling conversational AI agents that can be integrated with multiple channels, including websites and mobile apps. It supports the Bot Framework SDK, which enables the agent to handle order tracking, returns, and FAQs through natural language understanding (NLU) with LUIS or the newer CLU service.

Answer

Azure Logic Apps

Answer

Azure API Management

Answer

Azure Functions

Question 11

A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?

Accepted Answer

Azure AI Content Safety and Azure OpenAI content filtering.. Azure AI Content Safety provides built-in models for detecting harmful content such as hate speech, self-harm, and sexual content, while Azure OpenAI content filtering applies configurable severity-level filters (e.g., low, medium, high) to model inputs and outputs. Together, they enable real-time monitoring of toxicity and bias in generated investment advice, meeting compliance requirements for financial services.

Answer

Azure Cognitive Search and Azure AI Language.

Answer

Azure Bot Service and Azure Logic Apps.

Answer

Azure Machine Learning and Azure Functions.

Question 12

A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?

Accepted Answer

Provide better function descriptions with examples of when to use each function.. Providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.

Answer

Fine-tune the model on a dataset of correct function calls.

Answer

Reduce the number of functions to only the most common ones.

Answer

Set the temperature parameter to 0 for deterministic output.

Question 13

A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?

Accepted Answer

Pre-process the image to adjust contrast and brightness before calling the OCR API.. Azure AI Vision's OCR API performs best on images with sufficient contrast and brightness. Pre-processing the image (e.g., using OpenCV or PIL to adjust contrast and brightness) enhances text visibility, directly improving OCR accuracy for low-contrast images without changing the API or training a custom model.

Answer

Use a different OCR API from Azure Cognitive Services.

Answer

Train a custom OCR model using Azure Custom Vision.

Answer

Increase the confidence threshold for text detection.

Question 14

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

Accepted Answer

Index the documents into a vector database like Azure Cognitive Search.. Option C is correct because RAG requires an external knowledge store that supports semantic similarity search, and indexing the documents into a vector database such as Azure Cognitive Search (or Azure AI Search) creates embeddings that let the agent retrieve the most relevant passages at query time. Option E is correct because the defining step of Retrieval Augmented Generation is retrieving the top-k relevant document chunks and injecting them into the prompt as grounding context before the model generates its answer, which keeps responses accurate and current without retraining. Option A is not appropriate because no model can memorize an entire large document library, and relying on memorization defeats the purpose of retrieval. Option B is not appropriate because fine-tuning teaches style and task behavior rather than reliably storing and retrieving factual document content, and it is not the RAG mechanism. Option D is not appropriate because training a custom language model from scratch is prohibitively expensive and unnecessary when Azure OpenAI plus retrieval already solves the problem.

Answer

Ensure the model is large enough to memorize the entire document library.

Answer

Fine-tune the Azure OpenAI model on the document library.

Answer

Train a custom language model from scratch.

Question 15

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

Accepted Answer

The token limit is too low, truncating the function definitions.. Option B is correct because function definitions are serialized into the prompt sent to the model, so if the token limit (max_tokens/context window) is too low, the function schema can be truncated and the model cannot emit valid function calls. Option C is correct because Azure OpenAI function calling relies on a strict JSON Schema for each function's parameters; incorrect or incomplete schemas (wrong types, missing required fields) cause the model to produce arguments that fail validation or the call to be rejected. Option D is correct because the model selects functions based on their natural-language descriptions, so ambiguous or missing descriptions lead to wrong or no function selection. Option A is not a primary cause: temperature affects randomness, not whether a syntactically valid function call is produced, and function calling can work at high temperature. Option E is not a required check: while newer models may improve function-calling reliability, an outdated model version is not a standard diagnostic factor for functions failing to execute.

Answer

The temperature parameter is set too high.

Answer

The model version is outdated.

Question 16

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 17

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 18

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 19

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 20

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.

AI-102 Implement agentic AI solutions — All Questions With Answers

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

Match each Azure AI pricing tier to its description.

An agent built with Azure AI Foundry Agent Service must call an internal REST API that requires an OAuth 2.0 token. The security team refuses to store long-lived secrets in the agent definition and wants short-lived, scoped credentials issued at run time. Which approach should you implement?

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

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

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?

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

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?

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?

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?

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?

AI-102 Implement agentic AI solutions — All Questions With Answers

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

Match each Azure AI pricing tier to its description.

An agent built with Azure AI Foundry Agent Service must call an internal REST API that requires an OAuth 2.0 token. The security team refuses to store long-lived secrets in the agent definition and wants short-lived, scoped credentials issued at run time. Which approach should you implement?

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

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

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?

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

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?

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?

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?

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?