AI-102 Practice Test 48 — 15 Questions

Question 1

You are designing an Azure AI solution that uses Azure AI Search. The solution must ensure that only authorized users can query the search index, and that users can only access documents they are permitted to see. You need to implement document-level access control. What should you do?

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

Accepted Answer

Implement security filters in queries by using the user's identity and a field in the index that maps to allowed groups.. To enforce document-level access control in Azure AI Search, you must implement security filters. This involves adding a field to the index that specifies which users or groups can access each document, and then applying a filter in queries based on the authenticated user's identity. This ensures that users only retrieve documents they are permitted to see.

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Enable Azure Private Link for the search service and restrict access to the corporate network.

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Use customer-managed keys (CMK) to encrypt the index and control access to the keys.

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Use Azure role-based access control (RBAC) to assign the Search Index Data Reader role to users.

Question 2

You are configuring an Azure AI Search indexer to process documents from Azure Blob Storage. The documents include PDFs and Microsoft Word files. You need to extract both text and metadata such as author and creation date. Which indexer configuration should you use?

Accepted Answer

Set the parsingMode to default. The default parsingMode in Azure AI Search indexers is designed to handle a variety of document formats, including PDF and Microsoft Office files. It uses built-in document cracking to extract text and metadata, such as author and creation date, which are then available for mapping to index fields. Other parsing modes are specialized for JSON, delimited text, or plain text and do not support rich document extraction.

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Set the parsingMode to json

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Set the parsingMode to delimitedText

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Set the parsingMode to text

Question 3

You are building a generative AI application with Azure OpenAI Service. The application must use your own product catalog data stored in an Azure AI Search index to ground model responses. You need to configure the model deployment so that retrieved documents are automatically injected into the prompt without you manually assembling the context in application code. What should you configure on the model deployment?

Accepted Answer

Add a 'data source' configuration (your Azure AI Search index) to the model deployment by using the Azure OpenAI 'On Your Data' feature.. The Azure OpenAI On Your Data feature lets you attach a supported data source, such as an Azure AI Search index, directly to a model deployment. At inference time the service performs retrieval, injects the relevant chunks into the prompt, and can return citations. This avoids writing custom retrieval and prompt-assembly logic while grounding responses in the product catalog.

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Increase the deployment's tokens-per-minute quota so that the entire product catalog can be sent in each request.

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Enable content filtering on the deployment to restrict responses to catalog topics.

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Set the model temperature to 0 so the model always returns deterministic answers from the catalog.

Question 4

A company wants to build a solution that summarizes long support call transcripts into concise paragraphs. The transcripts are stored as plain text in Azure Blob Storage. You plan to use the summarization feature in Azure AI Language. Which summarization type should you use to generate a short paragraph that captures the main points of each transcript?

Accepted Answer

Abstractive summarization. Abstractive summarization in Azure AI Language generates new, concise text that captures the essence of the input, making it suitable for creating short paragraphs from long transcripts. Extractive summarization only selects existing sentences, key phrase extraction returns terms, and named entity recognition identifies entities. Only abstractive summarization produces the rephrased, paragraph-style summary required.

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Extractive summarization

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Key phrase extraction

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Named entity recognition

Question 5

You are developing a generative AI application using Azure OpenAI Service. The application must generate summaries of customer emails and then extract action items. You want to minimize the number of API calls and ensure the model outputs structured JSON. What should you do?

Accepted Answer

Use a single prompt that asks the model to summarize the email and extract action items, and specify the output format as JSON in the prompt.. The most efficient method is to use a single prompt that instructs the model to both summarize the email and extract action items, with the output specified as JSON. This reduces API calls to one, lowers latency, and provides structured data for easy parsing. It leverages the model's ability to handle multiple instructions and format outputs as requested.

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Fine-tune the model to output JSON with both summary and action items.

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Use the Azure OpenAI function calling feature to define a function that returns both summary and action items.

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Use two separate prompts: one for summarization and one for action item extraction, and combine the results in code.

Question 6

You are building a solution that analyzes customer feedback in real-time using Azure AI Language. The feedback is streamed from a web app and must be processed within 1 second. You need to extract sentiment and key phrases. Which service should you use?

Accepted Answer

Azure AI Language synchronous API. The synchronous API is built for immediate analysis of short texts, making it ideal for real-time sentiment and key phrase extraction from streaming feedback. Asynchronous and batch options introduce delays, and containers are not optimized for low-latency cloud-based streaming.

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Azure AI Language asynchronous API

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Azure AI Language container

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Azure AI Language batch processing with Azure Blob Storage

Question 7

A company wants to build a mobile app that recognizes and tags landmarks in photos taken by tourists. They need a prebuilt model that requires no training and can identify thousands of famous places worldwide. Which Azure AI Vision feature should they use?

Accepted Answer

Image Analysis with the Landmarks feature. The Landmarks feature in Image Analysis is a prebuilt model that recognizes thousands of famous landmarks and returns their names. It requires no training and is designed exactly for scenarios like tagging tourist photos. Other options either require training, are for different purposes, or do not provide specific landmark names.

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Face API with the Identify feature to match faces to famous people

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Computer Vision with the Describe feature to generate captions of the photos

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Custom Vision with an object detection project trained on landmark images

Question 8

You are developing a solution that uses Azure AI Vision to analyze images of products on an assembly line. You need to detect whether each product has a specific logo and also read the serial number printed on it. Which two Azure AI Vision services should you use? (Choose two.)

Accepted Answer

Azure AI Custom Vision. To detect a specific logo and read a serial number, you need two distinct capabilities: custom object detection and text extraction. Custom Vision can be trained to recognize the logo, while the Read API extracts the serial number from the image. Together, they fulfill both requirements without unnecessary complexity.

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Azure AI Document Intelligence

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Azure AI Vision Spatial Analysis

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Azure AI Face API

Question 9

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.

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

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

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

Question 10

You are designing a knowledge mining solution that must extract tables from scanned invoices stored in Azure Blob Storage and make the table cells searchable. The invoices are in PDF and JPEG formats. Which Azure AI service should you use to extract the tables before loading the data into Azure AI Search?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence provides prebuilt and custom models that detect tables and return structured cell data with row and column indices. It accepts PDF and image inputs, which matches the scanned invoice formats. The extracted table structure can then be shaped and indexed into Azure AI Search so that table cells are searchable.

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Azure AI Language

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Azure AI Search OCR skill

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Azure AI Vision Image Analysis

Question 11

You are developing an Azure AI solution that uses Azure Cognitive Search. The solution must index documents from an Azure Blob Storage container. You need to ensure that the indexer can access the Blob Storage container securely without storing credentials in the indexer definition. What should you do?

Accepted Answer

Enable a managed identity for the Azure Cognitive Search service and grant it access to the Blob Storage container.. A managed identity for Azure Cognitive Search is the correct approach because it allows the indexer to authenticate to Blob Storage without storing any credentials in the indexer definition. You assign the managed identity the appropriate role on the storage account. This is a secure, Azure-native method. The other options either store credentials (SAS token or account key) or use the wrong key for authentication.

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Use a shared access signature (SAS) token for the Blob Storage container and include it in the indexer's data source connection string.

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Store the Blob Storage account key in Azure Key Vault and reference it in the indexer definition using a Key Vault secret URI.

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Configure the indexer to use the Azure Cognitive Search service's API key to authenticate to Blob Storage.

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.

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

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

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

Question 13

You are building a custom question answering solution using Azure AI Language. The knowledge base contains a large number of question-answer pairs. You need to ensure that the solution returns the most relevant answer for a user's query, even when the query contains synonyms not present in the knowledge base. What should you do?

Accepted Answer

Add alternative questions for each answer that include common synonyms.. Custom question answering matches user queries to questions in the knowledge base. To handle synonyms, you can add alternative questions that include common synonyms for each answer. This improves the likelihood that a query with different wording will match the correct answer. Other options do not directly address synonym matching and may not improve relevance.

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Enable the 'enableHierarchicalExtraction' setting in the project configuration.

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Set the 'defaultAnswer' to a response that asks the user to rephrase using known terms.

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Increase the number of documents in the knowledge base to provide more context.

Question 14

You are building an Azure AI Search enrichment pipeline that extracts key phrases from documents stored in Azure Blob Storage. The documents are plain text files in English. You need to add a built-in skill that identifies the main concepts in each document without writing custom code. Which skill should you use?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is a built-in Azure AI Search skill that analyzes text and returns a list of key phrases representing the main concepts. It requires no custom code and integrates directly into a skillset. The other skills serve different purposes: entity recognition focuses on named entities, language detection identifies language, and translation converts languages.

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Entity Recognition skill

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Language Detection skill

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Text Translation skill

Question 15

A healthcare portal must summarize patient feedback stored in Azure Blob Storage. Documents are plain UTF-8 text and the summaries must be generated without sending content to a public endpoint. You are building the pipeline with Azure AI Language and a private network. Which configuration should you use to call extractive summarization while meeting the network requirement?

Accepted Answer

Create an Azure AI Language resource with a private endpoint and disable public network access, then call the REST API from a client inside the virtual network.. Meeting a no-public-endpoint requirement means the Azure AI Language resource must be reachable only through private networking. Combining a private endpoint with disabled public network access forces all calls, including extractive summarization, to travel inside the virtual network, which keeps document content off the public internet while preserving full API functionality.

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Deploy the open-source summarization container for Azure AI Language on an Azure Kubernetes Service cluster and call its local endpoint.

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Create a standard Azure AI Language resource and authenticate with an account key, relying on TLS to protect the content in transit.

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Use the Azure AI Language resource with a service tag firewall rule that allows only the portal's app service outbound IP addresses.