AI-102 Practice Exam 51 — 20 Questions

Question 1

You are building a generative AI solution with Azure OpenAI Service that must produce concise, factual answers using only a supplied knowledge base. During testing, the model sometimes invents details not present in the knowledge base. You need to reduce hallucinations without retraining the model. What should you do?

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

Accepted Answer

Lower the temperature and include a system message that instructs the model to answer only from the provided context and to say it does not know when the answer is absent.. Hallucinations in grounded generation are commonly reduced with prompt engineering: lower temperature to make output more deterministic, and a system message that restricts answers to the provided context and instructs the model to admit when the answer is not present. These changes need no retraining and can be applied immediately to the deployment.

Answer

Increase the max_tokens setting so the model has more room to explain its reasoning.

Answer

Switch to a larger model deployment with a bigger context window.

Answer

Fine-tune the base model on the knowledge base so it memorizes the facts.

Question 2

You are configuring an agent in Azure AI Foundry Agent Service to generate responses based on a large set of internal documents. The documents are stored in an Azure Storage account. You need to ensure the agent can retrieve relevant information from these documents to answer user queries. What should you do?

Accepted Answer

Create an Azure AI Search index that contains the documents and connect it to the agent as a knowledge source.. For large document sets, Azure AI Search provides scalable indexing and retrieval. By creating an index and connecting it as a knowledge source, the agent can query the index and retrieve relevant passages. This is the recommended approach for grounding agent responses in extensive internal documents.

Answer

Upload the documents to the agent's file storage and enable the file search tool.

Answer

Use the Azure AI Foundry SDK to embed the documents into the agent's prompt by concatenating their contents.

Answer

Configure the agent to use a custom tool that calls the Azure Storage REST API to fetch documents on demand.

Question 3

You are using Azure AI Language's custom question answering feature to build a FAQ bot. The knowledge base contains many question-and-answer pairs. Users sometimes ask questions that are paraphrases of the stored questions. You need to improve the likelihood that the bot returns the correct answer for paraphrased questions. Which action should you take?

Accepted Answer

Add alternate questions to each question-and-answer pair in the knowledge base.. Adding alternate questions to each question-and-answer pair is the most direct way to improve matching for paraphrased queries. The service uses these variants to better understand different ways users might ask the same thing. Raising the threshold, accepting all active learning suggestions, or trying to change the embedding model do not reliably address the need to match paraphrases.

Answer

Enable active learning and accept all suggested question variants.

Answer

Switch the knowledge base to use a different language model for embeddings.

Answer

Increase the confidence threshold for answer returns to 90.

Question 4

You are building an agent with the Azure AI Agents SDK that must support multi-turn conversations for a help desk scenario. The agent should remember details a user provided earlier in the same conversation, such as their device model. You need to manage conversation state. What should you do?

Accepted Answer

Create a thread for the conversation and add messages to it as the user and agent exchange turns.. Threads are the Azure AI Agents SDK mechanism for multi-turn state. By creating a thread and adding each message to it, the agent has access to prior turns and can recall details like a device model. The other approaches either bypass platform state management, misuse instructions, or apply a retrieval tool to a problem that requires conversation history.

Answer

Include the entire conversation history in every request and omit thread creation.

Answer

Enable a file search tool so the agent can look up the device model from uploaded files.

Answer

Store the device model in the agent's instructions each time the user mentions it.

Question 5

You are building a web application that lets users upload photos of restaurant menus and receive the extracted text. The menus are often photographed at an angle, with uneven lighting and background clutter. You need an Azure AI Vision capability that returns text lines and words with bounding boxes, and you want to minimize development effort. Which Azure AI Vision feature should you use?

Accepted Answer

Azure AI Vision OCR (Read) API. The Read API in Azure AI Vision is purpose-built for OCR and returns text lines and words with bounding boxes, making it ideal for extracting menu content from photos. It handles real-world conditions like skew and clutter. The other services either return high-level labels, detect objects, or analyze faces, none of which extract the literal text required by the application.

Answer

Azure AI Custom Vision object detection model

Answer

Azure AI Vision Image Analysis with the Tags feature

Answer

Azure AI Face API with the OCR attribute enabled

Question 6

You are troubleshooting a Microsoft Copilot Studio agent that calls a custom connector action to retrieve shipment data. The action works in the test canvas, but when the agent is published to a channel, the action returns an authorization error for some users. The connector uses OAuth. You need to resolve the issue. What should you do?

Accepted Answer

Verify that each user has consented to the OAuth connection and has permission in the source system, then have them sign in to the connection.. OAuth-protected connectors require each user to consent and to have appropriate rights in the target system. The test canvas often runs under the maker's credentials, which masks per-user authorization problems. Confirming consent and permissions, and having affected users sign in to the connection, fixes the errors without weakening the security model.

Answer

Increase the connector's timeout setting so authorization calls have more time to complete.

Answer

Republish the agent to the channel so the connector definition refreshes for all users.

Answer

Change the connector authentication to API key and share the key with all users.

Question 7

You are deploying a generative AI application using Azure OpenAI Service. The application must generate responses that are creative and varied for a marketing campaign. Which parameter should you adjust to increase the randomness of the model's output?

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of the model's output. Higher temperatures lead to more diverse and creative responses, which is suitable for a marketing campaign that requires varied content. Other parameters like top-p and penalties affect diversity in different ways but are not the primary control for randomness.

Answer

Presence penalty

Answer

Top-p (nucleus sampling)

Answer

Frequency penalty

Question 8

You are developing a solution that uses Azure AI Language to detect the language of incoming text messages. The messages may contain mixed languages within a single document. You need to ensure the API returns the detected language and a confidence score for each document. Which request should you make?

Accepted Answer

POST to the analyze-text endpoint with kind set to LanguageDetection.. Language detection is performed by setting the kind parameter to LanguageDetection in the analyze-text request. This returns the detected language and a confidence score for each document. Other tasks like EntityRecognition, KeyPhraseExtraction, and SentimentAnalysis serve different purposes and do not provide language detection.

Answer

POST to the analyze-text endpoint with kind set to EntityRecognition.

Answer

POST to the analyze-text endpoint with kind set to KeyPhraseExtraction.

Answer

POST to the analyze-text endpoint with kind set to SentimentAnalysis.

Question 9

You are building an Azure AI Search enrichment pipeline that processes PDF documents from Azure Blob Storage. The documents contain both text and images. You need to extract text from the images and also detect the language of the extracted text to route documents to language-specific processing. Which two built-in skills should you include in the skillset? (Choose two.)

Accepted Answer

OCR skill. The OCR skill is required to extract text from images within the PDFs. The Language Detection skill is required to identify the language of the extracted text for routing. Together, they enable the pipeline to process image-based text and then determine its language. The other skills do not provide these capabilities: key phrase extraction, entity recognition, and translation serve different purposes.

Answer

Text Translation skill

Answer

Entity Recognition skill

Answer

Key Phrase Extraction skill

Question 10

You are building a generative AI assistant with Azure OpenAI Service that must answer questions using a large corpus of internal product manuals. The manuals are updated weekly, and the assistant must reflect changes without retraining the model. You need to implement a retrieval-augmented generation (RAG) pattern. What should you use to index and retrieve relevant manual content?

Accepted Answer

Create an Azure AI Search index with vector embeddings of the manual chunks and use it as a data source in the Azure OpenAI 'on your data' configuration.. The correct approach is to use Azure AI Search with vector embeddings as a data source for Azure OpenAI 'on your data'. This enables retrieval-augmented generation, where relevant manual chunks are retrieved and supplied to the model, ensuring responses reflect weekly updates without retraining. It is the supported, scalable method for grounding generative AI in dynamic internal documents.

Answer

Use Azure Cosmos DB with the built-in vector search and connect it directly to the model via a function calling tool.

Answer

Store the manuals as plain text in Azure Blob Storage and pass the entire corpus in each prompt.

Answer

Fine-tune the base model on the manuals each week using Azure OpenAI fine-tuning.

Question 11

You are designing an Azure AI Search enrichment pipeline that extracts entities from text using the Entity Recognition skill. You need to ensure that the extracted entities are stored as a collection in the index so that users can filter and facet on them. Which index field type should you use?

Accepted Answer

Collection(Edm.String). The Entity Recognition skill outputs a list of strings representing entities. To store this list and enable filtering and faceting, the index field must be a collection of strings. Collection(Edm.String) is the correct type because it accommodates multiple values and supports the necessary query capabilities. Other types either cannot hold multiple values or are not designed for text data.

Answer

Edm.ComplexType

Answer

Edm.String

Answer

Edm.Int32

Question 12

You are implementing a generative AI chat solution on Azure OpenAI Service. The solution must ground its answers in a large internal knowledge base and return inline citations that link back to the exact source chunks. You want to minimize custom code and use a managed retrieval pipeline. Which Azure OpenAI feature should you configure?

Accepted Answer

Use the On Your Data feature with an Azure AI Search index and set the citation output to include document references.. Grounding answers in private data with verifiable citations requires a retrieval pipeline. Azure OpenAI On Your Data with an Azure AI Search index provides managed ingestion, chunking, retrieval, and citation output, so answers link to the actual source chunks. Prompt instructions or model tuning alone cannot retrieve documents or guarantee accurate references.

Answer

Deploy a fine-tuned model trained on the internal knowledge base and use it for chat completions.

Answer

Set the temperature parameter to a low value and enable content filtering on the deployment.

Answer

Create a system message that instructs the model to answer only from the knowledge base and to include source names.

Question 13

You are developing a generative AI application using Azure OpenAI. The application must log all prompts and completions for auditing, but sensitive data such as personal health information (PHI) must be redacted before logging. Which approach should you use?

Accepted Answer

Use Azure AI Language's PII detection to identify and redact PHI in prompts and completions before writing to your logging store.. To audit prompts and completions while protecting PHI, integrate Azure AI Language PII detection into your logging pipeline. Redact sensitive entities before persisting logs. This satisfies auditing requirements and compliance mandates, whereas relying on diagnostic logs or storage encryption alone would leave PHI exposed.

Answer

Enable Azure OpenAI diagnostic settings to send logs to Azure Monitor, and rely on built-in PHI redaction.

Answer

Configure Azure OpenAI to not log prompts and completions, and instead log only token counts and model names.

Answer

Store logs in an Azure Storage account with encryption at rest and enable soft delete.

Question 14

You are planning to deploy an Azure AI solution that uses Azure Cognitive Services. You need to ensure that the solution can be deployed to multiple regions and that each region uses a separate endpoint. What should you do?

Accepted Answer

Deploy multiple Cognitive Services accounts, one per region, and configure the application to use the appropriate endpoint.. To have separate endpoints per region, you must deploy a Cognitive Services account in each region. Each account provides a unique endpoint and key. This approach also allows for regional failover if needed. The application must be designed to select the correct endpoint based on the region.

Answer

Deploy a single Cognitive Services account and enable the multi-region feature.

Answer

Deploy a single Cognitive Services account and use the same endpoint for all regions.

Answer

Deploy a single Cognitive Services account and use Azure Traffic Manager to route requests to different regions.

Question 15

You are building a solution that must detect the sentiment polarity (positive, negative, neutral, or mixed) of incoming customer support tickets written in English and German. You want to use a single Azure AI Language resource and avoid maintaining separate models per language. Which approach should you use?

Accepted Answer

Call the Sentiment Analysis feature of Azure AI Language with the language parameter set to 'auto' so the service detects the language and returns sentiment for both English and German text.. The prebuilt Sentiment Analysis capability in Azure AI Language natively supports multiple languages and can automatically detect the input language. Pointing a single resource at both English and German tickets avoids the overhead of separate resources, translation pipelines, or custom model training while still returning polarity and confidence scores.

Answer

Create two separate Azure AI Language resources, one configured for English and one for German, and route tickets based on a language detection step.

Answer

Train a custom sentiment model in Azure AI Language using labeled German and English tickets, then deploy it as a custom single-label classification project.

Answer

Use the Translator service to convert German tickets to English, then call Sentiment Analysis only on the translated English text.

Question 16

You are building a generative AI solution with Azure OpenAI Service that must produce concise, factual answers using only a supplied knowledge base. During testing, the model sometimes invents details not present in the knowledge base. You need to reduce hallucinations without retraining the model. What should you do?

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

Accepted Answer

Use Azure OpenAI with a retrieval-augmented generation pattern that queries an up-to-date search index of the documents.. Retrieval-augmented generation with a search index allows the model to access current document content at inference time. When documents change, you update the index, and the model retrieves the latest passages. This avoids fine-tuning and ensures responses reflect the most recent information, which is critical for frequently updated knowledge bases.

Answer

Fine-tune the Azure OpenAI model each time the documents are updated.

Answer

Increase the model's temperature setting to encourage more creative and up-to-date responses.

Answer

Embed the documents directly into the model's prompt by concatenating all of them in each request.

Question 17

A logistics company runs an Azure AI Agent Service agent that must call an internal REST API to book delivery slots. The API requires a per-tenant OAuth token that must never be visible in run logs or agent definitions. The agent is invoked by many tenants through your own web application. Which approach should you use to give the agent access to the API securely?

Accepted Answer

Define the API as an OpenAPI tool with a project connection that holds the credential, and pass the tenant identity as a non-secret parameter.. Using an OpenAPI tool with a project connection keeps credentials server-side while still letting the agent decide when to invoke the booking API. The tenant identifier travels as an ordinary parameter, so logs remain free of secrets and per-tenant authorization is resolved by the connection at call time rather than by the model.

Answer

Store the tenant token in the agent's instructions and let the model include it in the function call arguments.

Answer

Have the agent emit the tenant ID, then let your web application call the booking API directly and append the response as a user message.

Answer

Create a function tool that returns the tenant token to the model so the model can place it in the next function call.

Question 18

You are deploying an Azure AI solution that uses Azure AI Search. The solution must be able to index data from an Azure SQL database and provide search results to an application. You need to ensure that the search service can access the database securely without storing credentials in the indexer definition. What should you do?

Accepted Answer

Configure the Azure AI Search service to use a managed identity and grant it access to the Azure SQL database.. Using a managed identity for the Azure AI Search service and granting it access to the Azure SQL database allows the indexer to authenticate without storing credentials. This is the most secure and recommended approach for service-to-service authentication in Azure.

Answer

Store the Azure SQL database connection string in Azure Key Vault and reference it in the indexer definition.

Answer

Use SQL authentication with a username and password, and encrypt the connection string using Azure AI Search's built-in encryption.

Answer

Configure the Azure SQL database to allow access from all Azure services and use a firewall rule.

Question 19

A company uses Azure AI Vision to extract text from scanned invoices. They need to preserve the layout information, such as tables and key-value pairs, to automate data entry. Which Azure service should they use?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Document Intelligence provides prebuilt invoice models that extract text, tables, and key-value pairs while preserving layout. The Read API only returns text lines, and Custom Vision and Face API are for different domains. For automating data entry from invoices with tables, Document Intelligence is the appropriate service.

Answer

Azure AI Face API

Answer

Azure AI Custom Vision

Answer

Azure AI Vision Read API

Question 20

You are designing a solution that uses Azure AI Language custom question answering. The knowledge base contains 200 question-answer pairs. Users report that the bot sometimes returns answers to questions that are semantically similar but not actually asked. You need to reduce these false positives while maintaining the ability to answer paraphrased questions. What should you configure?

Accepted Answer

Increase the confidence threshold score for the project.. The confidence threshold determines the minimum score required for an answer to be returned. By increasing it, the bot becomes more selective, reducing false positives from semantically similar questions. Active learning and alternative questions improve coverage but do not directly filter low-confidence matches. The default answer is only a fallback and does not affect matching behavior.

Answer

Set the 'default answer' to a custom message indicating no answer was found.

Answer

Enable the 'Enable active learning' option.

Answer

Add more alternative questions to each question-answer pair.