AI-102 Practice Test 47 — 15 Questions

Question 1

Your team is building an internal knowledge assistant with Azure OpenAI Service. Legal requires that every generated answer include a traceable reference to the source document so reviewers can verify claims. The documents already live in Azure AI Search, and you want the service to return citation metadata alongside the generated text. Which feature should you configure?

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

Accepted Answer

The On Your Data feature configured with an Azure AI Search data source, which returns citations with the response.. On Your Data integrates an Azure AI Search index as the grounding source and returns the model's answer together with citation information identifying the retrieved documents. Because the documents already live in Azure AI Search and the requirement is traceable references in the output, this feature directly provides the needed attribution without custom retrieval code.

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Azure OpenAI content filtering with annotating models enabled on the deployment.

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A system message instructing the model to always mention the file name it used to answer.

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Logging every request and response to Azure Monitor and querying the logs after the fact.

Question 2

You are designing an Azure AI solution that uses an Azure OpenAI resource. The solution must allow developers to call the model from a web app without embedding API keys in client-side code. You need to ensure that the web app can authenticate to the Azure OpenAI resource. What should you implement?

Accepted Answer

Enable managed identity on the web app and assign the Cognitive Services OpenAI User role to the identity on the Azure OpenAI resource.. Using a managed identity with the appropriate Azure RBAC role allows the web app to authenticate to Azure OpenAI without any secrets. The Cognitive Services OpenAI User role provides the necessary permissions to invoke the model. This approach is secure, scalable, and aligns with Azure best practices for keyless authentication.

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Configure the web app to use the Azure OpenAI API key and rotate it every 24 hours using Azure Automation.

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Store the Azure OpenAI key in Azure Key Vault and have the web app retrieve it at runtime.

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Generate a shared access signature (SAS) token for the Azure OpenAI resource and pass it in the request header.

Question 3

You deploy a GPT-4o model in Azure OpenAI Service for an internal assistant that summarizes long contracts. Legal requires that every generated summary be traceable to the exact source passages and that the assistant never answer from general world knowledge. You need the model to ground each statement in retrieved text and expose the supporting passages to the caller. What should you configure?

Accepted Answer

Use the Azure OpenAI On Your Own Data feature with citations enabled. Grounding the assistant in an indexed corpus and returning citations is what makes each summary statement traceable to a specific contract passage. Azure OpenAI On Your Own Data performs retrieval against Azure AI Search, injects the retrieved chunks, and returns citation metadata with the response. Sampling parameters, output length, and monitoring logs change behavior or observability but never bind statements to sources.

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Enable the abuse monitoring logging option on the Azure OpenAI resource

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Increase the model's max_tokens value to fit the entire contract

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Set temperature to 0 and top_p to 1 on the chat completions call

Question 4

A company wants to build a conversational interface that can understand user intents and extract key information from utterances, such as booking a flight to a specific city on a specific date. They need to implement this using Azure AI Language. Which feature should they use?

Accepted Answer

Conversational language understanding (CLU). Conversational language understanding (CLU) is the Azure AI Language feature built for intent recognition and entity extraction in conversational input. It supports custom intents and entities, making it ideal for building chatbots and voice assistants that need to parse user requests like flight bookings. Other features lack intent understanding or custom entity extraction.

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Custom text classification

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Named entity recognition (NER)

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Key phrase extraction

Question 5

You are using Azure AI Custom Vision to detect defects in fabric rolls. After training an object detection model, you notice that it often misses small tears. You have a large dataset of labeled images, but the tears are very small relative to the image size. What should you do to improve the model's detection of small tears?

Accepted Answer

Train a new model with images where the tears are larger in the frame. Small object detection is challenging because the objects occupy few pixels. By training with images where the tears are larger in the frame—through cropping or zooming—you provide the model with more detailed features to learn from. This approach directly addresses the issue and improves the model's ability to detect small tears.

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Increase the number of training iterations

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Use the 'General' domain instead of 'Product' domain

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Increase the model's confidence threshold

Question 6

You are building an internal assistant on Azure OpenAI that must stream responses to a React web app while hiding the model endpoint key from the browser. The app already authenticates users with Microsoft Entra ID. You need the least-privilege approach that keeps the key out of client code. What should you do?

Accepted Answer

Create an Azure API Management instance that fronts Azure OpenAI, and have the React app call API Management with the user's Entra ID token.. The key must never reach the browser, so a server-side intermediary is required. Azure API Management can authenticate the Entra ID token, enforce policies, and inject the Azure OpenAI key from a secure store while preserving streaming responses. Managed identities cannot run in a browser, and retrieving a key client-side exposes it regardless of where it was stored.

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Give the React app a system-assigned managed identity and call Azure OpenAI directly from the browser.

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Store the Azure OpenAI key in Azure Key Vault and have the React app retrieve it at runtime using the signed-in user's token.

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Embed the Azure OpenAI key in the React build and call the completions endpoint directly from the browser.

Question 7

You are deploying a generative AI assistant on Azure OpenAI Service that must summarize user-supplied financial reports. Security policy requires that report contents never leave your Azure tenant and that the model must not be fine-tuned. You need to provision the resource so that prompt and completion data are not retained for human review and are not used to train any shared model. What should you configure?

Accepted Answer

Submit an Azure OpenAI limited access modification request to disable abuse monitoring for the subscription.. The requirement is about data handling by the service itself, not about access control or content safety. The only mechanism that removes default human review and retention of prompts and completions for an Azure OpenAI resource is an approved limited access modification for abuse monitoring on the subscription. Content filters, diagnostic settings, and RBAC all address different concerns and leave the retention behavior unchanged.

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Set the Azure OpenAI resource's 'Content logging' to Disabled in Azure Monitor diagnostic settings.

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Create the deployment in a separate resource group and assign the Cognitive Services User role only to the application's managed identity.

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Deploy the model with a content filter policy set to block high-severity categories and enable prompt shields.

Question 8

A logistics company uses an Azure AI Search indexer to process bills of lading stored in Azure Blob Storage. The indexer uses a skillset with a ShaperSkill that builds a complex object named 'shipment' containing nested fields for carrier, origin, and destination. After a full index run, queries for the carrier field return no results even though the source documents contain the data. You need to make the carrier value searchable. What should you do?

Accepted Answer

Add an outputFieldMapping that maps the nested carrier value to a top-level index field.. The ShaperSkill constructs a complex object inside the enrichment tree, but that tree is transient. Persisting any part of it requires an outputFieldMapping from the enrichment node to a field in the index. Mapping the nested carrier node to a searchable top-level field is what actually makes the value appear in query results.

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Change the ShaperSkill to output a string instead of an object.

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Increase the indexer batch size so that all documents are processed in a single run.

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Enable the indexer's cache and rerun the indexer.

Question 9

A company uses Azure AI Search to index documents from an Azure SQL Database. They have configured an indexer with a skillset that includes a custom skill hosted in an Azure Function. The custom skill enriches each document with a 'category' field. After running the indexer, they notice that the 'category' field is missing in the index for all documents. The Azure Function logs show that it is receiving requests and returning responses. What is the most likely cause?

Accepted Answer

The output field mapping for the custom skill is not correctly configured in the indexer.. The most likely cause is a missing or incorrect output field mapping. The custom skill executes and returns a response, but if the indexer is not configured to map that output to an index field, the enriched data never gets stored. This is a common configuration oversight: the skill definition includes an output, but the indexer's outputFieldMappings must explicitly associate it with a target field.

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The indexer's data source connection string is invalid, so documents are not being retrieved.

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The custom skill's context is set to /document/pages/*, but the documents do not have a 'pages' node.

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The custom skill's Azure Function is returning a 200 OK status but with an empty response body.

Question 10

You are developing an agent by using the Azure AI Foundry Agent Service. The agent must query a proprietary internal REST API that returns JSON data. The API requires an OAuth 2.0 access token for authentication. You need to configure the agent to call this API. What should you do?

Accepted Answer

Configure an OpenAPI tool for the agent, specifying the API's OpenAPI specification and setting up an OAuth 2.0 connection for authentication.. The agent must call an OAuth-protected REST API. Azure AI Foundry Agent Service supports OpenAPI tools, which allow you to import an API specification and configure OAuth 2.0 authentication. This enables the agent to securely call the API, with the service handling token acquisition and refresh. Other options either lack secure authentication support or are not designed for dynamic API invocation.

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Use the Azure AI Foundry SDK to programmatically inject the OAuth token into each request by using a custom middleware component.

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Add the API endpoint as a knowledge source in Azure AI Search and use integrated vectorization to index the JSON responses.

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Create a custom tool by using a function calling definition that includes the API endpoint and authentication details, and register it with the agent.

Question 11

Your team is using the Azure OpenAI Batch API to process 200,000 product-description rewrites overnight. The job must complete within a fixed window and cost as little as possible, and results are not needed interactively. A developer reports that the submitted batch job failed with an error about the input file. What is the most likely cause?

Accepted Answer

The input file was uploaded as a single large JSON array containing all 200,000 requests.. The Batch API expects a JSONL input file in which every line is an independent request object with a unique custom_id. Submitting one large JSON array violates that contract, so the job is rejected during input validation before any processing begins.

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The batch job used a deployment with a lower tokens-per-minute quota than the interactive deployment.

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The input file was uploaded as a JSONL file with one request object per line and a unique custom_id for each line.

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The batch job was submitted without specifying a completion window, so it defaulted to the maximum allowed.

Question 12

You are building an Azure AI Search enrichment pipeline that must extract text and layout information from scanned PDFs stored in Azure Blob Storage. The extracted content must include bounding boxes for each text line so that a downstream custom skill can associate key-value pairs spatially. You need to add a built-in skill to the skillset to perform this extraction. Which skill should you add?

Accepted Answer

DocumentIntelligenceLayoutSkill. The requirement is to extract text and layout information, including bounding boxes, from scanned PDFs. DocumentIntelligenceLayoutSkill is a built-in Azure AI Search skill that uses Azure AI Document Intelligence to analyze document structure and return bounding boxes for text lines. This enables downstream spatial association. Other skills either do not provide layout data or are not designed for direct PDF analysis, so they fail to meet the spatial requirement.

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DocumentExtractionSkill

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TextMergeSkill

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OcrSkill

Question 13

You are planning an Azure AI solution that will process documents containing personal data. The solution uses Azure AI Document Intelligence and Azure OpenAI in the same subscription. Corporate governance requires that data processed by these services never leave a specified geographic region and that you can audit which operations were performed on the data. You need to select a deployment approach that meets these requirements with the least administrative effort. What should you do?

Accepted Answer

Deploy all resources in the required region and enable diagnostic settings to send logs to a Log Analytics workspace in the same region.. Placing all resources in the required region ensures data is processed within that geography, and diagnostic settings stream resource logs and metrics to Log Analytics for auditing. This uses native platform features with little ongoing effort. Multi-region routing, cross-region key vaults, and soft delete do not satisfy both residency and auditability.

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Deploy resources in the required region and configure customer-managed keys stored in a key vault in a different region.

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Deploy resources in the required region and enable soft delete on all storage accounts used by the solution.

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Deploy resources in multiple regions and use Azure Traffic Manager to route requests to the closest region.

Question 14

You are building a generative AI solution with Azure OpenAI Service that uses the GPT-4 model. The solution must process user requests and call external APIs to retrieve real-time data. You need to ensure the model can invoke the correct API based on user intent and return the results in a structured format. Which feature should you implement?

Accepted Answer

Function calling with the 'tools' parameter in the Chat Completions API.. Function calling is the native feature in Azure OpenAI that allows the model to request invocation of external functions with structured arguments. By defining tools, the model can determine when to call an API and with what parameters. This provides reliable integration with external systems and returns structured responses, fulfilling the requirement.

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Prompt engineering with few-shot examples of API calls.

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Using the Assistants API with code interpreter enabled.

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Fine-tuning the model on a dataset of API calls and responses.

Question 15

You are creating an Azure AI Search solution that must extract named entities such as people, organizations, and locations from text documents. You want to use a built-in cognitive skill to perform this extraction during indexing. Which skill should you add to the skillset?

Accepted Answer

EntityRecognitionSkill. EntityRecognitionSkill is the built-in Azure AI Search skill that extracts named entities and classifies them into categories such as persons, organizations, and locations. It is part of the cognitive skills library and is designed to enrich documents during indexing. The other skills perform different natural language processing tasks such as key phrase extraction, language detection, or translation, none of which provide categorized entity extraction.

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KeyPhraseExtractionSkill

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LanguageDetectionSkill

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TextTranslationSkill